The interaction between mothers and young children is a highly dynamic process neurally characterized by inter-brain synchrony (IBS) at θ and/or α rhythms. However, their establishment, dynamic changes, and roles in mother-child interactions remain unknown. In this study, through a simultaneous dynamic analysis of inter-brain EEG synchrony, intra-brain EEG power, and interactive behaviors from 40 mother-preschooler dyads during turn-taking cooperation, we constructed a dynamic inter-brain model that θ-IBS and α-IBS alternated with interactive behaviors, with EEG frequency-shift as a prerequisite for IBS transitions. When mothers attempt to track their children's attention and/or predict their intentions, they will adjust their EEG frequencies to align with their children's θ oscillations, leading to a higher occurrence of the θ-IBS state. Conversely, the α-IBS state, accompanied by the EEG frequency-shift to the α range, is more prominent during mother-led interactions. Further exploratory analysis reveals greater presence and stability of the θ-IBS state during cooperative than non-cooperative conditions, particularly in dyads with stronger emotional attachments and more frequent interactions in their daily lives. Our findings shed light on the neural oscillatory substrates underlying the IBS dynamics during mother-preschooler interactions.

Automatic exploit generation (AEG) refers to the automatic discovery and exploitation of vulnerabilities against unknown targets. Traditional AEG often targets a single type of vulnerability and still relies on templates built from expert experience. To achieve intelligent exploit generation, we establish a comprehensive benchmark using Binary Exploitation (pwn) challenges in Capture the Flag (CTF) competitions and investigate the capabilities of Large Language Models (LLMs) in AEG based on the benchmark. To improve the performance of AEG, we propose PwnGPT, an LLM-based automatic exploit generation framework that automatically solves pwn challenges. The structural design of PwnGPT is divided into three main components: analysis, generation, and verification modules. With the help of a modular approach and structured problem inputs, PwnGPT can solve challenges that LLMs cannot directly solve. We evaluate PwnGPT on our benchmark and analyze the outputs of each module. Experimental results show that our framework is highly autonomous and capable of addressing various challenges. Compared to direct input LLMs, PwnGPT increases the completion rate of exploit on our benchmark from 26.3% to 57.9% with the OpenAI o1-preview model and from 21.1% to 36.8% with the GPT-4o model.

Acoustic motion tracking has been viewed as a promising user interaction technique in many scenarios such as Virtual Reality (VR), Smart Appliance, video gaming, etc. Existing acoustic motion tracking approaches, however, suffer from long window of accumulated signal and time-consuming signal processing. They are inherently difficult to achieve both high accuracy and low delay. In this paper, we present Vernier, an efficient and accurate acoustic tracking method based on commodity mobile devices. We design a new approach to efficiently and accurately derive phase change and thus moving distance. Vernier significantly reduces the tracking delay/overhead by removing the complicated frequency analysis and long window of signal accumulation, while keeping a high tracking accuracy. We implement Vernier on Android, and evaluate its performance with COTS mobile devices including Samsung Galaxy S7 and Sony L50t. Experimental results show that Vernier outperforms previous approaches with a tracking error less than 4 mm. The tracking speed achieves 3× improvement to the previous phase based approaches and 10× to Doppler Effect based approaches. Vernier is also validated in applications like controlling and drawing, and we believe it is generally applicable in many real applications.

Acoustic based tracking has been shown promising in many applications like Virtual Reality, smart home, video gaming, etc. Its real life deployments, however, face fundamental limitations. Existing approaches generally need three sound sources, while most COTS devices (e.g., TVs) and speakers have only two sound sources. We present AcouRadar, an acoustic-based localization system with single sound source. In the heart of AcouRadar we adopt a general new model which quantifies signal properties of different frequencies, distances and angles to the source. We verify the model and show that signal from a single source can provide features for localization. To address practical challenges, (1) we design an online model adaption method to address model deviation from real signal, (2) we design pulse modulated signals to alleviate the impact of environment such as multipath effect, and (3) to address signal dynamics over time, we derive relatively stable amplitude ratio between different frequencies, and thus provide a spectrum based localization method. We implement AcouRadar on Android and evaluate its performance for different COTS speakers in different environments. The results show that the model for localization can be generalized to different speakers. AcouRadar can achieve single source localization with average error less than 5 cm and average angle error of 1.76°.

Acoustic motion tracking has been viewed as a promising user interaction technique in many scenarios such as Virtual Reality (VR), Smart Appliance, video gaming, etc. Existing acoustic motion tracking approaches, however, suffer from long window of accumulated signal and time-consuming signal processing. Consequently, they are inherently difficult to achieve both high accuracy and low delay. We propose Vernier, an efficient and accurate acoustic tracking method on commodity mobile devices. In the heart of Vernier lies a novel method to efficiently and accurately derive phase change and thus moving distance. Vernier significantly reduces the tracking delay/overhead by removing the complicated frequency analysis and long window of signal accumulation, while keeping a high tracking accuracy. We implement Vernier on Android, and evaluate its performance on COTS mobile devices including Samsung Galaxy S7 and Sony L50t. Evaluation results show that Vernier outperforms previous approaches with a tracking error less than 4 mm. The tracking speed achieves 3×improvement to existing phase based approaches and 10×to Doppler Effect based approaches. Vernier is also validated in applications like controlling and drawing, and we believe it is generally applicable in many real applications.

Ultrasound based tracking and localization are more and more popular in recent years. However, to achieve 3D tracking or localization, at least three sound sources are needed. Unfortunately, there are only two sound sources available in most scenarios in daily life. To address this problem, we propose SOLO (Single sound sOurce LOcalization with single microphone), a novel approach to infer 2D information from single sound source. We found that sound with different frequencies from a signal source may have different strength distribution in different areas. Each area has a unique group of sound strengths of different frequencies. So we can take this question as a classify question. Based on this, we first obtain the signal strength in different frequencies by using STFT (Short-Time Fourier Transform). Next we measure 1D distance with traditional phase-based distance measuring approach. Then we take 1D distance and the sound strengths as features to a neural network to classify the sample into different classes, each representing a region in 2D space. With our system, we can distinguish 3 × 3 regions (5 cm × 5 cm for each region) with only single sound source and single microphone. SOLO is the first work to achieve 2D localization and tracking with only single sound source and single microphone as far as we know. This work make it possible to realize 3D tracking or localization with only two sound sources.