In reverse engineering interfacing with C/C++ functions is of great interest because it provides much more flexibility for product development and security purpose. However, it has been a great challenge when interfacing functions with user defined calling conventions due to the lack of sufficient and user-friendly tooling. In this work, we design and implement Clang __usercall, which aims to provide programmers with an elegant and familiar syntax to specify user defined calling conventions on functions in C/C++ source code. Our key novelties lie in mimicing the most popular syntax and adapting Clang for interfacing purpose. Our preliminary user study shows that our solution outperforms the existing ones in multiple key aspects including user experience and required lines of code. Clang __usercall is already added to the Compiler Explorer website as well.

With the advancement of deep neural networks and computer vision-based Human Activity Recognition, employment of Point-Cloud Data technologies (LiDAR, mmWave) has seen a lot interests due to its privacy preserving nature. Given the high promise of accurate PCD technologies, we develop, PALMAR, a multiple-inhabitant activity recognition system by employing efficient signal processing and novel machine learning techniques to track individual person towards developing an adaptive multi-inhabitant tracking and HAR system. More specifically, we propose (i) a voxelized feature representation-based real-time PCD fine-tuning method, (ii) efficient clustering (DBSCAN and BIRCH), Adaptive Order Hidden Markov Model based multi-person tracking and crossover ambiguity reduction techniques and (iii) novel adaptive deep learning-based domain adaptation technique to improve the accuracy of HAR in presence of data scarcity and diversity (device, location and population diversity). We experimentally evaluate our framework and systems using (i) a real-time PCD collected by three devices (3D LiDAR and 79 GHz mmWave) from 6 participants, (ii) one publicly available 3D LiDAR activity data (28 participants) and (iii) an embedded hardware prototype system which provided promising HAR performances in multi-inhabitants (96%) scenario with a 63% improvement of multi-person tracking than state-of-art framework without losing significant system performances in the edge computing device.

Human activity recognition (HAR) is a an important field of study concerned with identifying the specific movement or action of a person based on sensor data. Movements are often typical activities, such as walking, lying, standing, and sitting which has high significance in assessing health related vital signs. While accuracy of HAR been improved significantly using advanced deep learning techniques, employment of LiDAR (Light detection and Ranging) has been rarely explored due to the high cost of sensor device as well as the complexity of LiDAR point-cloud data. In this paper, we propose LAMAR (LiDAR-based Adaptive Multi-inhabitant Activity Recognition), a low-resolution low-cost (<300$) solid-state LiDAR-based multiple-inhabitant activity recognition system. LAMAR incorporates a series of existing signal processing techniques and a novel adaptive deep learning model to improve multi-inhabitant HAR performance. In this regard, at first, LAMAR posits a voxelized feature representation and clustering based multiple person tracking method to separate individual person related point-clouds. Then, it employes a novel variational autoencoder based domain adaptation technique to improve activity recognition performance by transferring domain knowledge from high resolution to low resolution LiDAR data. Our experimental evaluation on two datasets (one real-time collected data and another publicly available available data) provided significant improvement of HAR performance (94%) in multiple-inhabitant scenario with 63% improvement of multiple person tracking than existing work.