Mine fleet management algorithms can significantly reduce operational costs and enhance productivity in mining systems. Most current fleet management algorithms are evaluated based on self-implemented or proprietary simulation environments, posing challenges for replication and comparison. This paper models the simulation environment for mine fleet management from a complex systems perspective. Building upon previous work, we introduce probabilistic, user-defined events for random event simulation and implement various evaluation metrics and baselines, effectively reflecting the robustness of fleet management algorithms against unforeseen incidents. We present "OpenMines", an open-source framework encompassing the entire process of mine system modeling, algorithm development, and evaluation, facilitating future algorithm comparison and replication in the field. Code is available at https://github.com/370025263/openmines.

In recent years, research on Vehicle-to-Everything (V2X) cooperative perception algorithms mainly focuses on the fusion of intermediate features from LiDAR point clouds. Since the emergence of excellent single-vehicle visual perception models like BEVFormer, collaborative perception schemes based on camera and late-fusion have become feasible approaches. This paper proposes a V2X-collaborative 3D object detection structure in Bird's Eye View (BEV) space, based on global non-maximum suppression and late-fusion (V2X-BGN), and conducts experiments on the V2X-Set dataset. Focusing on complex road conditions with extreme occlusion, the paper compares the camera-based algorithm with the LiDAR-based algorithm, validating the effectiveness of pure visual solutions in the collaborative 3D object detection task. Additionally, this paper highlights the complementary potential of camera-based and LiDAR-based approaches and the importance of object-to-ego vehicle distance in the collaborative 3D object detection task.

Mini-programs (MiniApps), lightweight versions of full-featured mobile apps that run inside a host app such as WeChat, have become increasingly popular due to their simplified and convenient user experiences. However, MiniApps raise new security and privacy concerns as they can access partially or all of host apps' system resources, including sensitive personal data. While taint detection has been proven effective in addressing this kind of concerns, existing taint detection techniques for mobile apps cannot be directly applied to MiniApps. The main reason is that the key logics of MiniApps are usually written in J avaScript, and its intrinsic characteristics (function-level scope, dynamic types, synchronous programming, and code obfuscation) prevent existing taint detection techniques from precisely propagating the taints. To address this problem, we propose a novel taint detection technique, Wemint, that detects sensitive information leaks in MiniApps. Specifically, Wemint facilitates taint propagation via building a context-based model based on the operational prin-ciple of MiniApps and J avaScript, and addresses asynchronous function calls by modeling their callbacks explicitly in taint rules. In addition, due to the adoption of Abstract Syntax Trees (ASTs) for code representation during taint detection, Wemint exhibits better robustness against the commonly-applied code obfuscation. Our experimental results show that Wemint can effectively detect sensitive information leaks in WeChat MiniApps, as well as trace the path of sensitive data flows. By applying Wemint to over 20K suspicious MiniApps, we found that over 7.5K (36.5 %) of them have sensitive data leaks, and Wemint outperforms the state-of-the-art DoubleX based techniques in detecting these leaks.

Optical flow estimation is a basic task in self-driving and robotics systems, which enables to temporally interpret traffic scenes. Autonomous vehicles clearly benefit from the ultra-wide Field of View (FoV) offered by 360° panoramic sensors. However, due to the unique imaging process of panoramic cameras, models designed for pinhole images do not directly generalize satisfactorily to 360° panoramic images. In this paper, we put forward a novel network framework——PANO FLOW, to learn optical flow for panoramic images. To overcome the distortions introduced by equirectangular projection in panoramic transformation, we design a Flow Distortion Augmentation (FDA) method, which contains radial flow distortion (FDA-R) or equirectangular flow distortion (FDA-E). We further look into the definition and properties of cyclic optical flow for panoramic videos, and hereby propose a Cyclic Flow Estimation (CFE) method by leveraging the cyclicity of spherical images to infer 360° optical flow and converting large displacement to relatively small displacement. PanoFlow is applicable to any existing flow estimation method and benefits from the progress of narrow-FoV flow estimation. In addition, we create and release a synthetic panoramic dataset FlowScape based on CARLA to facilitate training and quantitative analysis. PanoFlow achieves state-of-the-art performance on the public OmniFlowNet and the fresh established FlowScape benchmarks. Our proposed approach reduces the End-Point-Error (EPE) on FlowScape by 27.3%. On OmniFlowNet, PanoFlow achieves an EPE of 3.17 pixels, a 55.5% error reduction from the best published result (7.12 pixels). We also qualitatively validate our method via an outdoor collection vehicle and a public real-world OmniPhotos dataset, indicating strong potential and robustness for real-world navigation applications. Code and dataset are publicly available at PanoFlow.

Label distribution learning is a gradually emerging field of learning that can address label ambiguity. In a sense, label distribution learning includes common single-label learning and multi-label learning with broader applicability. For each instance, label distribution learning uses the label distribution to reflect the individual label importance. Previous studies have shown that label distribution learning is an effective method to address label ambiguity. In order to strengthen the learning performance and make the prediction of the distribution more accurate, this paper proposes a label distribution learning algorithm called SRPLDL. We propose a metric called the Square Root Pearson, and then we build our learning model on the basis of this metric. Comparative experiments on 11 real label distribution datasets prove that the SRPLDL algorithm can solve the label distribution learning problem and outperforms other algorithms.