3D object detection is an indispensable component of autonomous driving. Due to the expensive and labor-intensive annotation required for full supervision, sparsely supervised 3D object detection is emerging as a promising alternative. Although some existing sparse supervision methods have achieved encouraging detection results, they do not perform well with distant or occluded objects. To address this issue, we propose a Consistency Regularization method for Sparsely-supervised 3D object detection(CRS3D). CRS3D consists of three modules: the Point Cloud Adjust module and the Consistency Loss module, which enhance the model’s ability to perceive distant objects by aligning predictions between the original and perturbed point clouds; and the Priori Ratio module, which optimizes the perception of occluded objects by imposing constraints based on priori information. In the KITTI benchmark, CRS3D achieves 87.4% 3D-mAP for the car class at easy difficulty using only 2% of the annotations, surpassing the accuracy of fully supervised methods.

RGB-D has gradually become a crucial data source for understanding complex scenes in assisted driving. However, existing studies have paid insufficient attention to the intrinsic spatial properties of depth maps. This oversight significantly impacts the attention representation, leading to prediction errors caused by attention shift issues. To this end, we propose a novel learnable Depth interaction Pyramid Transformer (DiPFormer) to explore the effectiveness of depth. Firstly, we introduce Depth Spatial-Aware Optimization (Depth SAO) as offset to represent real-world spatial relationships. Secondly, the similarity in the feature space of RGB-D is learned by Depth Linear Cross-Attention (Depth LCA) to clarify spatial differences at the pixel level. Finally, an MLP Decoder is utilized to effectively fuse multi-scale features for meeting real-time requirements. Comprehensive experiments demonstrate that the proposed DiPFormer significantly addresses the issue of attention misalignment in both road detection (+7.5%) and semantic segmentation (+4.9% / +1.5%) tasks. DiPFormer achieves state-of-the-art performance on the KITTI (97.57% F-score on KITTI road and 68.74% mIoU on KITTI-360) and Cityscapes (83.4% mIoU) datasets.

Abstract Educational Data Mining (EDM) has emerged as a vital field of research, which harnesses the power of computational techniques to analyze educational data. With the increasing complexity and diversity of academic data, Deep Learning techniques have shown significant advantages in addressing the challenges associated with analyzing and modeling this data. Existing studies are scattered across various domains, making it challenging to gain a comprehensive understanding of how Deep Learning techniques can transform educational practices. This survey aims to systematically review the state-of-the-art in EDM with Deep Learning. We begin by providing a brief introduction to EDM and Deep Learning, highlighting their relevance in the context of modern education. Next, we present a detailed review of Deep Learning techniques applied in four typical educational scenarios, including knowledge tracing, student behavior detection, performance prediction, and personalized recommendation. Furthermore, a comprehensive overview of public datasets and processing tools for EDM is provided. Finally, we point out emerging trends and future directions, aiming to guide researchers and practitioners in advancing the field of EDM.

Abstract In recent years, many few‐shot classification methods have been proposed. However, only a few of them have explored robust classification, which is an important aspect of human visual intelligence. Humans can effortlessly recognise visual patterns, including lines, circles, and even characters, from image data that has been corrupted or degraded. In this paper, the authors investigate a robust classification method that extends the classical paradigm of robust geometric model fitting. The method views an image as a set of points in a low‐dimensional space and analyses each image through low‐dimensional geometric model fitting. In contrast, the majority of other methods, such as deep learning methods, treat an image as a single point in a high‐dimensional space. The authors evaluate the performance of the method using a noisy Omniglot dataset. The experimental results demonstrate that the proposed method is significantly more robust than other methods. The source code and data for this paper are available at https://github.com/pengsuhua/PMF_OMNIGLOT .

Due to the sparsity and occlusion of point clouds, pure point cloud detection has limited effectiveness in detecting such samples. Researchers have been actively exploring the fusion of multimodal data, attempting to address the bottleneck issue based on LiDAR. In particular, virtual points, generated through depth completion from front-view RGB image, offer the potential for better integration with point clouds. Nevertheless, recent approaches fuse these two modalities in the region of interest (RoI), which limits the fusion effectiveness due to the inaccurate RoI region issue in the point cloud’s branch, especially in hard samples. To overcome it and unleash the potential of virtual points, while combining late fusion, we present leverage virtual point (LVP), a high-performance 3-D object detector which LVPs in early fusion to enhance the quality of RoI generation. LVP consists of three early fusion modules: virtual points painting (VPP), virtual points auxiliary (VPA), and virtual points completion (VPC) to achieve point-level fusion and global-level fusion. The integration of these modules effectively improves occlusion handling and improves the detection of distant small objects. In the KITTI benchmark, LVP achieves 85.45% 3-D mAP. As for large dataset nuScenes, we could improve the detection accuracy of large objects by compensating for errors in depth estimation. Without whistles and bells, these results establish LVP as an impressive solution for a 3-D outdoor object detection algorithm.

Semantic perception in driving scenarios plays a crucial role in intelligent transportation systems. However, existing Transformer-based semantic segmentation methods often do not fully exploit their potential in understanding driving scene dynamically. These methods typically lack spatial reasoning, failing to effectively correlate image pixels with their spatial positions, leading to attention drift. To address this issue, we propose a novel architecture, the Hierarchical Spatial Perception Transformer (HSPFormer), which integrates monocular depth estimation and semantic segmentation into a unified framework for the first time. We introduce the Spatial Depth Perception Auxiliary Network (SDPNet), a framework for multiscale feature extraction and multilayer depth map prediction to establish hierarchical spatial coherence. Additionally, we design the Hierarchical Pyramid Transformer Network (HPTNet), which uses depth estimation as learnable position embeddings to form spatially correlated semantic representations and generate global contextual information. Experiments on benchmark datasets such as KITTI-360, Cityscapes, and NYU Depth V2, demonstrate that HSPFormer outperforms several state-of-the-art networks, and achieves promising performance with 66.82% top-1 mIoU on KITTI-360, 83.8% mIoU on Cityscapes, and 57.7% mIoU on NYU Depth V2, respectively. The code will be made publicly available athttps://github.com/SY-Ch/HSPFormer.

When the number of available training views is limited, NeRF and 3DGS will soon overfit the optimization and learn the wrong scene geometry. For this challenge, a common solution is to provide depth prior as supervision to correct scene geometry. In this work, we present Geometric Regularized 3D Gaussian Splatting (GeoRGS), a priors-independent method for improving novel view synthesis from sparse inputs. We analyze the problems of the density control strategy in 3DGS with sparse inputs, and find that correcting the erroneous Gaussian growth trend at the beginning of training is effective in mitigating overfitting. Based on this analysis, we propose two geometric regularization methods that do not require prior information. One is based on selecting seed patches of 3D Gaussian from the scene, which guides growth to form correct scene geometry, while the other focuses on regularizing depth similarity between object surfaces and edges. GeoRGS achieves state-of-the-art performance in novel view synthesis from sparse input on LLFF, Blender, RealEstate10K and MipNeRF360 datasets, while also demonstrating significantly faster training speeds and rendering efficiency compared to other baselines.

High-performance and real-time road detection plays an essential role in Advanced Driver Assistance Systems (ADAS) of intelligent transportation. However, existing approaches still suffer from ambiguous road contour in traffic environment because deep learning methods lack explicit constraints on road boundaries with similar textures and structures. To address the unsatisfactory boundaries, we propose an efficient architecture for urban road detection to refine road edges adaptively. First, we design a lightweight symmetrical data-fusion network to merge spatial responses into visual features. Second, we construct cross-layer attention transformation to aggregate non-local contextual information. Moreover, a progressive uncertainty analysis module eliminates indistinct road and obstacle edges. Finally, we introduce upgrade uncertainty loss and improved deep supervision to constrain margin error for multi-scale predictions. Results of experiments using three famous datasets confirm the superiority of our method (F1-measure of 96.91% in KITTI, 98.86% in Cityscapes, and 95.18% in R2D, processing speed of 0.02s) over previous approaches. We demonstrate that, to ensure the safety of autonomous driving, the Epurate-Net adaptively refines road contour to reach exquisite road margins. The source code will be available soon.

In July 2022, the National Institute of Standards and Technology (NIST) announced its selection of four algorithms for post-quantum cryptography standardization in advance. Among these algorithms, Kyber was chosen as the only key encapsulation mechanism (KEM). In the Kyber KEM, the modular reduction function is utilized in numerous areas. We have discovered that by modeling controllable modular reduction functions, unknown modular reduction functions can be targeted. And attacks can then be constructed. Henceforth, profiling can be mounted on the target device. In this paper, we present a machine-learning-based key recovery attack on Kyber, without needing a reference device. We have effectively attacked the modular reduction function. Furthermore, this vulnerability that enables the reuse of the same function could be utilized in other attacks.

3-D object detection is a vital part of outdoor scene perception. Learning the complete size and accurate positioning of objects from an incomplete point cloud spatial structure is essential to 3-D object detection. We propose a novel flexible 3-D heatmap auxiliary network (3-D HANet) for object detection. To obtain complete structure and location information from an incomplete point cloud structure, we propose a 3-D heatmap to reflect object information. Also, we design a plug-and-play auxiliary network based on 3-D heatmap, which improves the accuracy of the entire detection network without extra computation in the inference stage. We validate the 3-D HANet on the basis of three classic 3-D object detection networks: PointPillars, sparsely embedded convolutional detection (SECOND), and structure aware single-stage 3-D object detection from point cloud (SASSD). Experimental results show that our auxiliary network augments the feature extraction ability of the backbone network, which is manifested in that the predicted boxes and the ground-truth boxes are more suitable in size and more aligned in direction. Furthermore, we conducted verification experiments on the state-of-the-art (SOTA) detector, CasA, and made a further improvement on the official ranking of the KITTI dataset.

Kyber is one of the four final Key Encapsulation Mechanism (KEM) competitors of the National Institute of Standards and Technology PostQuantum Cryptography standardization competition. This paper reveals the vulnerability of Kyber under a voltage template side channel attack: the modular polynomial subtraction operation in Kyber.CCAKEM.Dec. In this paper, by splicing data under different selected ciphertexts, a small number of traces are required to recover the secret key. Experiments show that the recovering accuracy of secret key achieves 100% when using 330 traces, and it still achieves 98% when only using 44 traces.

One-stage 3D object detection using mobile light detection and ranging (LiDAR) has developed rapidly in recent years. Specifically, one-stage methods have attracted attention because of their high efficiency and light weight compared with two-stage methods. Inspired by this, we present the semantic segmentation assisted one-stage three-dimensional vehicle object detection (SSA3D), a network for the rapid detection of objects that keeps the advantages of the semantic segmentation module in the two-stage methods without increasing redundant computational load. First, we modified the sampling of the farthest point to improve the quality of the sampling points. This helps to reduce sampling outlier points and bad points that are difficult to perceive in the spatial structure information surrounding the point. Second, a neighbor attention group module is devoted to selectively add extra weight to neighbor points because of the different importance of neighbor points for the corresponding sampling point. Correctly increasing the weight is helpful to obtain richer spatial structure information. Finally, a delicate box generation module is included as a voted center point layer based on the generalized Hoff vote method and an anchor-free regression. We used the feature aggregation module as the backbone and the feature propagation module as the auxiliary network to achieve efficiency. At the same time, the auxiliary network retains the ability to extract point-wise features from the state-of-the-art semantic segmentation network. In experiments, we evaluated and tested the SSA3D on a common KITTI dataset and achieved improved performance in the class of car accuracy.

Instance segmentation based on 3D point cloud is a key step in scene understanding. It is widely used in indoor robot navigation, outdoor autonomous driving, and other fields. But research in this area is still in its infancy. Instance segmentation not only needs to predict the semantic label of each point but also the instance label of each point. Therefore, semantic segmentation can be considered the basis of instance segmentation to some extent. Based on this motivation, we designed a voxel-based branch based on convertible sparse convolution and residual optimization modules. We design a point-based branch so that the network can maintain high-resolution representation. Then the two branches are combined to optimize the semantic segmentation results. Breadth-first search (BFS) performs well in indoor point clouds and is simple to operate. Therefore, we use this clustering operation to group the points of the same instance to obtain the instance segmentation result. The proposed method was tested on the indoor dataset Scan-Net v2 and achieved relatively good instance segmentation precision.

In recent years, deep learning methods make a big difference in side-channel attack (SCA) community especially in the profiled scenario. Multiclass classification method is the common way to complete such classification task. In this article, we propose a novel SCA method utilizing multilabel classification from bit-to-byte view. Accordingly, each leakage trace has eight labels when considering a byte. The experimental results on several datasets show that our multilabel classification method is efficient and even performs better in some situations compared with the original multiclass classification model while model complexity is much reduced. Besides, our multilabel model can be seen as ensemble of monobit models and we verify the ensemble effect experimentally.

Differential Fault Attack (DFA) is a powerful cryptanalytic technique to retrieve secret keys by exploiting the faulty ciphertexts generated during encryption procedure. This article proposes a novel DFA attack that is effective on ITUbee, a software-oriented block cipher for resource-constrained devices. Different from other DFA, our attack makes use of not only faulty values, but also differences between fault-free intermediate values corresponding to 2 plaintexts, which combine traditional differential analysis with DFA. The possible injection positions with different number of faults are discussed. The most efficient attack takes 2 25 round function operations with 4 faults, which is achieved in a few seconds on a PC.

Many linear structured genetic programming are proposed in the past years. Gene expression programming, as a classic linear represented genetic programming, is powerful in solving problems of data mining and knowledge discovery. Constrains of gene expression programming like head-tail mechanism do contribution to the legality of chromosome. however, they impair the flexibility and adaptability of chromosome to some extend. Inspired by the diversity of chromosome arrangements in biology, an unconstrained encoded gene expression programming is proposed to overcome above constraints. In this way, the search space is enlarged; meanwhile the parallelism and the adaptability are enhanced. A group of regression and classification experiments also show that unconstrained gene expression programming performs better than classic gene expression programming.