Syed Md Mukit Rashid, Abdullah Al Ishtiaq, Kai Tu, Yilu Dong, Tianwei Wu, Ali Ranjbar, Tianchang Yang, Najrin Sultana, Shagufta Mehnaz, Syed Rafiul Hussain. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

This paper introduces MCTrack, a new 3D multi-object tracking method that achieves performance across KITTI, nuScenes, and Waymo datasets. Addressing the gap in existing tracking paradigms, which often perform well on specific datasets but lack generalizability, MCTrack offers a unified solution. Additionally, we have standardized the format of perceptual results across various datasets, termed BaseVersion, facilitating researchers in the field of MOT) to concentrate on the core algorithmic development without the undue burden of data preprocessing. Finally, recognizing the limitations of current evaluation metrics, we introduce a novel set of metrics designed to evaluate the output of motion information, including velocity and acceleration, which are essential for subsequent tasks. The source codes of the proposed method are available at this link: https://github.com/megvii-research/MCTrack

Baseband firmware plays a critical role in cellular communication, yet its proprietary, closed-source nature and complex, stateful processing logic make systematic security testing challenging. Existing methods often fail to account for the interdependencies between baseband tasks and the statefulness of input processing logic, limiting their scope and effectiveness. We present Loris, a stateful fuzz testing frame-work designed to explore and analyze baseband firmware implementations effectively. We employ iterative symbolic analysis to progressively identify state variables and the predicates over them that define different protocol states, while alleviating the state explosion problem. It enables Loris to perform targeted exploration and fuzzing of program regions with high potential for vulnerabilities. We evaluated Loris across 5 commercial devices from two major vendors, covering both 4G Long-Term Evolution (LTE) and 5G New Radio (NR), demonstrating its broad applicability. Our testing revealed 7 new vulnerabilities exploitable by over-the-air attackers, potentially leading to baseband crashes, remote code execution, and denial of service.

This paper proposes Proteus, a protocol state machine, property-guided, and budget-aware automated testing approach for discovering logical vulnerabilities in wireless protocol implementations. Proteus maintains its budget awareness by generating test cases (i.e., each being a sequence of protocol messages) that are not only meaningful (i.e., the test case mostly follows the desirable protocol flow except for some controlled deviations) but also have a high probability of violating the desirable properties. To demonstrate its effectiveness, we evaluated Proteus in two different protocol implementations, namely 4G LTE and BLE, across 23 consumer devices (11 for 4G LTE and 12 for BLE). Proteus discovered 25 unique issues, including 112 instances. Affected vendors have positively acknowledged 14 vulnerabilities through 5 CVEs.

We propose a probabilistic generative model for extracting intended path shape qualities of an object moving under human control in real time. At each instant, we decide whether the object is moving in a straight, curved, or random path, or whether it has stopped moving. Our model incorporates sensor noise as well as human imperfections in the intended motion. As well as tracking the object's position, velocity, and motion direction, we compute the posterior probability of each shape quality hypothesis given all sensed-data in the horizon [t - N + 1,t]; the hypothesis maximizing this posterior is taken as the decision. The posterior is computed using the unscented Kalman filter (UKF), as our model is inherently nonlinear. The path-shape quality tracking is successfully embedded in a hybrid physical-digital interface where the position of an illuminated ball, sensed by a low-cost video camera array, triggers multimodal feedback in a mediated learning environment. We show successful results on a variety of real-world motion paths where the participant is given only verbal descriptions of how to move. Our generative model is further validated by user studies involving a simple color-based interaction, where participants discover shape quality controls as they interact.