Although existing 3D perception algorithms have demonstrated significant improvements in performance, their deployment on edge devices continues to encounter critical challenges due to substantial runtime latency. We propose a new benchmark tailored for online evaluation by considering runtime latency. Based on the benchmark, we build a Latency-Aware 3D Streaming Perception (LASP) framework that addresses the latency issue through two primary components: 1) latency-aware history integration, which extends query propagation into a continuous process, ensuring the integration of historical feature regardless of varying latency; 2) latency-aware predictive detection, a module that compensates the detection results with the predicted trajectory and the posterior accessed latency. By incorporating the latency-aware mechanism, our method shows generalization across various latency levels, achieving an online performance that closely aligns with 80% of its offline evaluation on the Jetson AGX Orin without any acceleration techniques.

Mining feature evolvable streams has gained increasing attention in recent years. However, most existing approaches are designed for stationary data streams (i.e., data streams without concept drifts) and often work with high time complexity due to the time-consuming optimization procedure. The two deficiencies thus largely limit its applications to real-world data stream scenarios. In this paper, we consider a more difficult but practical streaming setting: a data stream with both concept drifts and evolvable features. To this end, we propose a general framework for mining concept-drifting data streams with evolvable features, called FEMC, based on an efficient Feature Evolvable streaming learning and dynamic Micro-Clusters maintenance. Specifically, we derive a closed-form solution to preserve the information in vanished features by learning a weight vector on survival features. The evolving concepts, are further learnt by dynamically maintaining a set of micro-clusters with varying feature space on-the-fly. Empirical results on real-world data sets have demonstrated the benefits of the proposed framework on both clustering and classification tasks by comparing with state-of-the-art algorithms.

Discovering 1-day vulnerabilities in binary patches is worthwhile but challenging. One of the key difficulties lies in generating inputs that could reach the patched code snippet while making the unpatched program crash. In this paper, we named it as a target-oriented input generation problem or a ToIG problem for clarity. Existing solutions for the ToIG problem either suffer from path explosion or may get stuck by complex checks. In the paper, we present a new solution to improve the efficiency of ToIG which leverage a combination of a distance-based directed fuzzing mechanism and a dominator-based directed symbolic execution mechanism. To demonstrate its efficiency, we design and implement 1dVul, a tool for 1-day vulnerability discovering at binary-level, based on the solution. Demonstrations show that 1dVul has successfully generated inputs for 130 targets from a total of 209 patch targets identified from applications in DARPA Cyber Grant Challenge, while the state-of-the-art solutions AFLGo and Driller can only reach 99 and 107 targets, respectively, within the same limited time budget. Further-more, 1dVul runs 2.2X and 3.6X faster than AFLGo and Driller, respectively, and has confirmed 96 vulnerabilities from the unpatched programs.