Hidden wireless cameras pose significant privacy threats, necessitating effective detection and localization methods. However, existing localization solutions often require impractical activity spaces, expensive specialized devices, or pre-collected training data, limiting their practical deployment. To address these limitations, we introduce CamLopa, a training-free wireless camera localization framework that operates with minimal activity space constraints using low-cost, commercial-off-the-shelf (COTS) devices. CamLopa can achieve detection and localization in just 45 seconds of user activities with a Raspberry Pi board. During this short period, it analyzes the causal relationship between wireless traffic and user movement to detect the presence of a hidden camera. Upon detection, CamLopa utilizes a novel azimuth localization model based on wireless signal propagation path analysis for localization. This model leverages the time ratio of user paths crossing the First Fresnel Zone (FFZ) to determine the camera's azimuth angle. Subsequently, CamLopa refines the localization by identifying the camera's quadrant. We evaluate CamLopa across various devices and environments, demonstrating its effectiveness with a 95.37% detection accuracy for snooping cameras and an average localization error of 17.23°, under the significantly reduced activity space requirements and without the need for training. Our code and demo are available at https://github.com/CamLoPA/CamLoPA-Code.

Pulmonary function testing is a crucial examination for respiratory diseases. Current medical spirometers are bulky and inconvenient, while available portable spirometers are extremely expensive and often lack accuracy. Furthermore, both devices require direct contact, inevitably increasing the cross-infection risk. To tackle these challenges, we propose Wi-Pulmo, an end-to-end deep learning-based Wireless System that utilizes WiFi channel state information (CSI) to provide contact-free, convenient, cost-effective, and precise pulmonary function testing outside the clinical setting. Based on the analysis of thoracic and abdominal movement patterns, Wi-Pulmo first validates the feasibility of using WiFi to estimate pulmonary function. Then, Wi-Pulmo designs an efficient fine-grained sensing quality-based algorithm for complete exhalation segmentation. Additionally, a relevant interference-tolerant learning algorithm based on variational inference is proposed to accurately map the CSI of WiFi signals to pulmonary function. Extensive experiments achieved average monitoring error rates of 2.59% for normal subjects in daily scenarios and 5.87% for real patients in tertiary hospitals over a two-month period. These satisfactory results demonstrate the strong effectiveness and robustness of Wi-Pulmo. Furthermore, our findings in clinical reveal a close correlation between chronic diseases and pulmonary function.

Recent years have witnessed a growing interest in Wi-Fi-based gesture recognition. However, existing works have predominantly focused on closed-set paradigms, where all testing gestures are predefined during training. This poses a significant challenge in real-world applications, as unseen gestures might be misclassified as known class during testing. To address this issue, we propose WiOpen, a robust Wi-Fi-based open-set gesture recognition (OSGR) framework. Implementing OSGR requires addressing challenges caused by the unique uncertainty in Wi-Fi sensing. This uncertainty, resulting from noise and domains, leads to widely scattered and irregular data distributions in collected Wi-Fi sensing data. Consequently, data ambiguity between classes and challenges in defining appropriate decision boundaries to identify unknowns arise. To tackle these challenges, WiOpen adopts a twofold approach to eliminate uncertainty and define precise decision boundaries. Initially, it addresses uncertainty induced by noise during data preprocessing by utilizing the channel state information (CSI) ratio. Next, it designs the OSGR network based on an uncertainty quantification method. Throughout the learning process, this network effectively mitigates uncertainty stemming from domains. Ultimately, the network leverages relationships among samples' neighbors to dynamically define open-set decision boundaries, successfully realizing OSGR. Comprehensive experiments on publicly accessible datasets confirm WiOpen's effectiveness.

Describing the dynamics of information diffusion within social networks poses a formidable challenge. Despite multiple endeavors aimed at addressing this issue, only a limited number of studies have effectively replicated and forecasted the evolving course of information diffusion. In this paper, we propose a novel model, DM-NAI, which not only considers the information transfer between adjacent users but also takes into account the information transfer between non-adjacent users to comprehensively depict the information diffusion process. Extensive experiments are conducted on six datasets to predict the information diffusion range and the diffusion trend of the social network. The experimental results demonstrate an average prediction accuracy range of 94.62% to 96.71%, respectively, significantly outperforming state-of-the-art solutions. This finding illustrates that considering information transmission between non-adjacent users helps DM-NAI achieve more accurate information diffusion predictions.

Modeling the information propagation process in social networks is a challenging problem. Despite numerous attempts to address this issue, existing studies often assume that user attitudes have only one opportunity to alter during the information propagation process. Additionally, these studies tend to consider the transformation of user attitudes as solely influenced by a single user, overlooking the dynamic and evolving nature of user attitudes and the impact of the public opinion environment. In this paper, we propose a novel model, UAPE, which considers the influence of the aforementioned factors on the information propagation process. Specifically, UAPE regards the user's attitude towards the topic as dynamically changing, with the change jointly affected by multiple users simultaneously. Furthermore, the joint influence of multiple users can be considered as the impact of the public opinion environment. Extensive experimental results demonstrate that the model achieves an accuracy range of 91.62% to 94.01 %, surpassing the performance of existing research.

Depression is a prevalent mental health disorder that significantly impacts individuals' lives and well-being. Early detection and intervention are crucial for effective treatment and management of depression. Recently, there are many end-to-end deep learning methods leveraging the facial expression features for automatic depression detection. However, most current methods overlook the temporal dynamics of facial expressions. Although very recent 3DCNN methods remedy this gap, they introduce more computational cost due to the selection of CNN-based backbones and redundant facial features. To address the above limitations, by considering the timing correlation of facial expressions, we propose a novel framework called FacialPulse, which recognizes depression with high accuracy and speed. By harnessing the bidirectional nature and proficiently addressing long-term dependencies, the Facial Motion Modeling Module (FMMM) is designed in FacialPulse to fully capture temporal features. Since the proposed FMMM has parallel processing capabilities and has the gate mechanism to mitigate gradient vanishing, this module can also significantly boost the training speed. Besides, to effectively use facial landmarks to replace original images to decrease information redundancy, a Facial Landmark Calibration Module (FLCM) is designed to eliminate facial landmark errors to further improve recognition accuracy. Extensive experiments on the AVEC2014 dataset and MMDA dataset (a depression dataset) demonstrate the superiority of FacialPulse on recognition accuracy and speed, with the average MAE (Mean Absolute Error) decreased by 21% compared to baselines, and the recognition speed increased by 100% compared to state-of-the-art methods. Codes are released at https://github.com/volatileee/FacialPulse.

Human pulmonary function declines with age. Elders, especially those with lung or cardiovascular diseases, yearn for daily lung function tests for timely diagnosis and treatment. However, current clinical spirometers are cumbersome and ex-pensive while home-use portable ones’ accuracy is questionable. Moreover, both kinds require contact measurements and could cause cross infection, especially hazardous for contagious diseases like COVID-19. To this end, we propose SpiroFi, a contactless system that leverages WiFi Channel State Information (CSI) for convenient yet accurate Pulmonary Function Testing (PFT) out of clinic. The key enabler underlying SpiroFi is a set of algorithms that can extract chest wall movement from WiFi signal variations and interpret such information into lung function indices. We have realized SpiroFi on low-cost commodity WiFi devices and tested it in a home-like site where it achieves 2.55% monitoring error over healthy youths. Then, with the Ethics Committee (EC) approval, we conducted a 2-month clinic study in a city hospital over elders with basic diseases. SprioFi still yields 6.05% monitoring error despite elders’ degenerated pulmonary function and body control. Also, the correlation between lung function and age as well as chronic diseases has been revealed, highlighting the importance of daily PFT for the elderly.