Large Language Models (LLMs) have showcased remarkable generalizability in language comprehension and hold significant potential to revolutionize human-computer interaction in smart homes. Existing LLM-based smart home assistants typically transmit user commands, along with user profiles and home configurations, to remote servers to obtain personalized services. However, users are increasingly concerned about the potential privacy leaks to the remote servers. To address this issue, we developHomeLLaMA, an on-device assistant for privacy-preserving and personalized smart home serving with a tailored small language model (SLM).HomeLLaMAlearns from cloud LLMs to deliver satisfactory responses and enable user-friendly interactions. Once deployed,HomeLLaMAfacilitates proactive interactions by continuously updating local SLMs and user profiles. To further enhance user interaction while protecting their privacy, we developPrivShieldto offer an optional, privacy-preserving LLM-based smart home service for users who are unsatisfied with local responses and are willing to send less-sensitive queries to remote servers. For evaluation, we develop a comprehensive benchmark,DevFinder, to assess service quality. Extensive experiments and user studies ($M=100$) demonstrate thatHomeLLaMAcan provide personalized services while significantly enhancing user privacy.

Android malware pose severe threats to the mobile application ecosystem. Although well-trained malware detection models can initially achieve satisfactory performance, they struggle with unseen Android apps constantly emerging over time, which is known as the concept drift problem. Previous methods frequently collect and label new apps to update the aging models. This process, however, necessitates domain knowledge and incurs prohibitive retraining overhead. To address this problem, this paper presents LLMalware, which integrates three novel technical components. First, we propose full-spectrum automated feature extraction, which automatically extracts diverse malware features from various detection models. Next, we develop cohesive feature fusion, which combines these features to build effective representations for robust malware detection. Lastly, we devise agile knowledge update to enable efficient online malware detection via an LLM-based automated agent and a dynamically maintained malware knowledge base. Extensive experiments demonstrate LLMalware can mitigate concept drift with an average improvement of approximately 10% in F1-score over state-of-the-art baselines.

Large Language Models (LLMs) exhibit remarkable language comprehension to revolutionize smart homes. Existing LLM-based smart home assistants typically transmit user commands, along with user profiles and home configurations, to remote servers to obtain personalized services. However, users are increasingly concerned about potential privacy leakage. To address this, we develop HomeLLaMA, an on-device assistant for privacy-preserving personalized smart homes with a tailored small language model (SLM). HomeLLaMA learns from cloud LLMs to deliver satisfactory responses and enable user-friendly interactions. Once deployed, HomeLLaMA facilitates proactive interactions by continuously updating local SLMs and user profiles. To further enhance user interaction while protecting privacy, we develop PrivShield to offer an optional privacy-preserving serving for those users who are unsatisfied with local responses and willing to send less-sensitive queries to remote servers. Experiments demonstrate HomeLLaMA provides satisfactory services while significantly enhancing user privacy.

Code Large Language Models (LLMs) enhance software development efficiency by automatically generating code and documentation based on user requirements. However, code LLMs cannot synthesize specialized programs when tasked with IoT applications that require domain knowledge. While Retrieval-Augmented Generation (RAG) offers a promising solution by fetching relevant domain knowledge, it necessitates powerful cloud LLMs (e.g., GPT-4) to process user requirements and retrieved contents, which raises significant privacy concerns. This approach also suffers from unstable networks and prohibitive LLM query costs. Moreover, it is challenging to ensure the correctness and relevance of the fetched contents. To address these issues, we propose GPIoT, a code generation system for IoT applications by fine-tuning locally deployable Small Language Models (SLMs) on IoT-specialized datasets. SLMs have smaller model sizes, allowing efficient local deployment and execution to mitigate privacy concerns and network uncertainty. Furthermore, by fine-tuning SLMs with our IoT-specialized datasets, the SLMs' ability to synthesize IoT-related programs can be substantially improved. To evaluate GPIoT's capability in synthesizing programs for IoT applications, we develop a benchmark, IoTBench. Extensive experiments and user trials demonstrate the effectiveness of GPIoT in generating IoT-specialized code, outperforming state-of-the-art code LLMs with an average task accuracy increment of 64.7% and significant improvements in user satisfaction.

Point clouds frequently contain noise and outliers, presenting obstacles for downstream applications. In this work, we introduce a novel denoising method for point clouds. By leveraging the latent space, we explicitly un-cover noise components, allowing for the extraction of a clean latent code. This, in turn, facilitates the restoration of clean points via inverse transformation. A key component in our network is a new multi-level graph convolution network for capturing rich geometric structural features at various scales from local to global. These features are then integrated into the invertible neural network which bijectively maps the latent space, to guide the noise disentanglement process. Additionally, we employ an invertible mono-tone operator to model the transformation process, effectively enhancing the representation of integrated geometric features. This enhancement allows our network to pre-cisely differentiate between noise factors and the intrinsic clean points in the latent code by projecting them onto separate channels. Both qualitative and quantitative evaluations demonstrate that our method outperforms state-of-the-art methods at various noise levels. The source code is available at https://github.com/yanbiaol/PD-LTS.

Performing accurate sensing in diverse environments is a challenging issue in wireless sensing technologies. Existing solutions usually require collecting a large number of samples to train a classifier for every environment, or further assume similar sample distribution between different environments such that a model trained in one environment can be transferred to another. In this paper, we propose RF-Siamese, an RFID-based gesture sensing approach that achieves comparable accuracy to existing solutions but requires only a few samples in each eivironment. RF-Siamese leverages Siamese networks to distinguish different gestures with only a small number of samples and is enhanced by several novel designs to achieve high accuracy in diverse environments. First, the network structure and parameters (e.g., loss function and distance metric) are carefully designed to be suitable for RFID gesture recognition. Second, a permutation-based dataset generation strategy is proposed to make full use of the collected samples to enhance the recognition accuracy. Third, a template matching method is proposed to extend the Siamese network to classify multiple gestures. Extensive experiments on commercial RFID devices demonstrate that RF-Siamese achieves a high accuracy of 0.93 with only one sample of each gesture when recognizing 18 different gestures, while state-of-the-art approaches based on transfer learning and meta learning achieve an accuracy of only 0.59 and 0.70, respectively.

Lip reading can help people with speech disorders to communicate with others and provide them with a new channel to interact with the world. In this paper, we design and implementHearMe, an accurate and real-time lip-reading system built on commercial RFID devices. HearMe can be used to accurately recognize different words in a pre-defined vocabulary without limitations in light conditions and can be used in multiple user scenarios by leveraging RFID's ability in identifying different users. We design an effective data collection strategy to well capture the tiny and complex signal patterns caused by mouth motion and propose a set of algorithms to extract signal profiles related to mouth motions and mitigate interference factors like multi-path. A carefully designed set of features, including time-domain statistical features and frequency-domain features, are then extracted from the signal to lift the recognition accuracy at the word level. To reduce training costs when the model is used in a new environment, a transfer-learning-based approach is adopted to enhance the robustness of the model in cross-environment scenarios. Experimental results show that HearMe detects speaking actions of the user with an accuracy higher than 0.95 and recognizes different words in a 20-words vocabulary with an average accuracy higher than 0.88. Moreover, the latency of HearMe ($\sim$150ms) is nearly two orders of magnitude less than traditional approaches, making it applicable to practical scenarios that require real-time lip reading.

Gesture recognition based on radio frequency identification (RFID) has attracted much research attention in recent years. Most existing RFID-based gesture recognition approaches use signal profile matching to distinguish different gestures, which incur large recognition latency and fail to support real-time applications. In this paper, we design and implement ReActor, a real-time and accurate gesture recognition system that recognizes a user's gestures with low latency and high accuracy even when the gestures'speed varies. ReActor combines the time-domain statistical features and the frequency-domain features to precisely represent the signal profile corresponding to different gestures. To maintain high accuracy across different environments, we preprocess the signals to remove reflection signals from surrounding objects and use only the signals related to gestures to train the classifier. Moreover, we train a classifier to predict the speed of the gesture and feed the extracted features to different classifiers according to the speed. We implement ReActor and evaluate its performance in different scenarios. Experimental results show that ReActor achieves an average accuracy of 97.2% in recognizing 18 different gestures with an average latency of 72 ms, more than two orders of magnitude faster than approaches based on profile template matching.

Barrier coverage is an important coverage model for intrusion detection, which requires a chain of sensors across the deployment area with the adjacent sensors’ sensing areas overlapping. Directional sensors are often dispersed from an airplane following a predetermined line. However, barrier coverage cannot be guaranteed after initial sensor deployment due to the sensors’ random offsets and random orientations. Fortunately, directional sensors can rotate to mend the barrier gaps using this line-based sensor deployment model. Existing work proposed a greedy heuristic approach to mend the gaps by rotating sensors, but it cannot answer whether there exists a barrier. We fill in this gap by presenting an exact algorithm which can determine whether there exists a barrier. We first introduce the notion of feasible orientation range and then try to calculate each sensor’s feasible orientation starting from the leftmost sensor. We also propose a fast algorithm of choosing the sensors’ orientations from their feasible orientation ranges to form a barrier if there exists a barrier, or form a set of sub-barriers if there does not exist a barrier. Simulation results show that our algorithm outperforms the distributed algorithm in the existing work.