Millimeter wave radar is gaining traction recently as a promising modality for enabling pervasive and privacy-preserving gesture recognition. However, the lack of rich and fine-grained radar datasets hinders progress in developing generalized deep learning models for gesture recognition across various user postures (e.g., standing, sitting), positions, and scenes. To remedy this, we resort to designing a software pipeline that exploits wealthy 2D videos to generate realistic radar data, but it needs to address the challenge of simulating diversified and fine-grained reflection properties of user gestures. To this end, we designG3Rwith three key components: i) agesture reflection point generatorexpands the arm's skeleton points to form human reflection points; ii) asignal simulation modelsimulates the multipath reflection and attenuation of radar signals to output the human intensity map; iii) anencoder-decoder modelcombines asampling moduleand afitting moduleto address the differences in number and distribution of points between generated and real-world radar data for generating realistic radar data. We implement and evaluateG3Rusing 2D videos from public data sources and self-collected real-world radar data, demonstrating its superiority over other state-of-the-art approaches for gesture recognition.

Millimeter wave radar is gaining traction recently for enabling privacy-preserving human sensing. However, the lack of large-scale, dynamic radar datasets impedes progress in developing robust and generalized deep learning models for mobile sensing applications. To address this problem, we resort to designing a software pipeline that leverages wealthy dynamic videos to generate synthetic radar data, but it faces two key challenges including i) incorrect camera and human positions leading to erroneous superposition of signal intensity and ii) the signal reflection of the background and humans in mobile scenes. To this end, we designMidas++to utilize rich videos to generate realistic radar data via two components: (i) ahuman mesh fitting and calibrationcomponent calculates the camera ego-motion parameters to calibrate the extracted human positions; (ii) areflection and noise signal estimationcomponent combines several key modules,depth prediction,reflection model, andspatiotemporal noise estimation, to output coarse radar data, followed by aU-Netmodel to generate realistic radar data. We implement and evaluateMidas++with video data from public data sources and real-world radar data, demonstrating thatMidas++outperforms other state-of-the-art approaches for both activity recognition and object detection tasks.

Binary source code matching, especially on function-level, has a critical role in the field of computer security. Given binary code only, finding the corresponding source code improves the accuracy and efficiency in reverse engineering. Given source code only, related binary code retrieval contributes to known vulnerabilities confirmation. However, due to the vast difference between source and binary code, few studies have investigated binary source code matching. Previously published studies focus on code literals extraction such as strings and integers, then utilize traditional matching algorithms such as the Hungarian algorithm for code matching. Nevertheless, these methods have limitations on function-level, because they ignore the potential semantic features of code and a lot of code lacks sufficient code literals. Also, these methods indicate a need for expert experience for useful feature identification and feature engineering, which is timeconsuming. This paper proposes an end-to-end cross-modal retrieval network for binary source code matching, which achieves higher accuracy and requires less expert experience. We adopt Deep Pyramid Convolutional Neural Network (DPCNN) for source code feature extraction and Graph Neural Network (GNN) for binary code feature extraction. We also exploit neural network-based models to capture code literals, including strings and integers. Furthermore, we implement "norm weighted sampling" for negative sampling. We evaluate our model on two datasets, where it outperforms other methods significantly.

The application of the null-field approach to radar cross section computations for composite objects is reviewed. The scattering problem is solved by means of a determination of the total transition matrix for the composite scatterer. It is noted that alternative approaches are usually available, which lead to different expressions for the transition matrix and which have different numerical characteristics. Results from the numerical implementation of these formulas are illustrated in a number of examples, and it is found that useful convergence can be achieved for a frequency interval that often extends into the resonance region. A number of ways of checking the quality of the results are also indicated.>