Advanced Persistent Threats (APTs) represent sophisticated cyberattacks that evade detection through stealthy, multistage operations, posing severe risks to critical infrastructure and organizational security. Due to their ability to effectively capture contextual information of attack behaviors, provenance graphs have emerged as a promising approach for APT detection. However, traditional binary edges in provenance graphs fail to represent the collaborative nature of APT attacks, where multiple entities coordinate in single operations, and local graph structures cannot capture the long-range dependencies across attack stages. To address these challenges, we propose HyperDetector, a novel hypergraph-based method for APT detection. First, we introduce hypergraph representation for provenance data, where hyperedges naturally connect multiple entities involved in system events, preserving the higher-order relational structures that characterize APT behaviors. Second, we employ block self-attention mechanisms that enable global reasoning across distant hypergraph regions, effectively linking dispersed attack indicators throughout the system. Through the synergistic integration of these approaches, HyperDetector achieves comprehensive understanding of both localized multi-entity collaborative behaviors and system-wide attack propagation patterns. Extensive evaluations across multiple prominent datasets demonstrate that HyperDetector outperforms state-of-the-art methods, showcasing its effectiveness for robust and holistic APT detection. Additionally, we make our code and datasets publicly available to facilitate reproducibility and foster further research in this critical area.

Vision-based tactile sensors have recently gained prominence due to their superior resolution and ability to capture multi-dimensional contact information. However, even when sensors share the same sensing principle, variations in production factors can lead to differences in the color patterns of tactile signals. Unlike common vision tasks, vision-based tactile perception depends on tracking light variation in colorful signals, making it more susceptible to lighting conditions and thus more prone to domain gaps. In this paper, we propose an Omni-hardness perception framework that enables adaptation across various vision-based tactile sensors. Firstly, in-depth analyses of the factors influencing the generalization of hardness perception are presented. Furthermore, the light balance module and the force scale module are coupled to regulate network learning of generalized representations. Experimental results across multiple sensors demonstrate the transferability of learned representations. Additionally, downstream tasks in natural object perception, tumor detection, and grasping stability prediction, are proposed to evaluate the potential applications. The framework's performance shows promise for advancing general tactile sensing and embodied tactile perception.

The booming self-driving cars need to understand the behaviors of other road users for better performance. Recognizing pedestrians’ crossing intentions is one of the most critical capabilities of self-driving cars to guarantee safe operations in the urban environment. Some researchers try to predict the future trajectories of pedestrians to avoid a potential collision. Others extract skeleton features from pedestrians to detect specific actions related to the crossing intention. All these methods either neglect the abundant appearance information of pedestrians, or work poorly under severe conditions, e.g., pedestrians standing far away, dim light, and occlusions. In this work, a pedestrian crossing intention recognition (PCIR) framework is proposed to recognize pedestrians’ crossing intention. A module for searching targets of interest is introduced to find the pedestrians who are likely to cross the streets, and perform scene perception simultaneously. An action recognition module utilizes a 3D convolutional neural network (CNN) to automatically extract spatiotemporal features that imply early actions before crossing, like limb movements. A distance encoding module makes full use of the contextual cues, e.g., distances between pedestrians and the oncoming vehicle, local traffic scenes around pedestrians, and vehicle speeds, to improve the recognition accuracy obtained from the action recognition module only. Finally, the PCIR framework fuses these factors to predict pedestrians’ crossing intentions by minimizing a focal loss. Experimental evaluations verify the effectiveness of the proposed method in recognizing pedestrians’ crossing intentions. Comparisons with the skeleton-based methods reveal the robustness of the PCIR framework in the urban environment.

Image classification with datasets that suffer from great imbalanced class distribution is a challenging task in computer vision field. In many real-world problems, the datasets are typically imbalanced and have a serious impact on the performance of classifiers. Although deep convolutional neural networks (DCNNs) have shown remarkable performance on image classification tasks in recent years, there are still few effective deep learning algorithms specifically for imbalanced image classification problems. To solve imbalanced image classification problem, in this paper, we explore a new deep learning algorithm called Squeeze-and-Excitation Deep Pyramidal Residual Network (SE-PyramidNet) combining with Generative Adversarial Network (GAN)-based curriculum learning. Firstly, we construct the refined Deep Pyramidal Residual Network by embedding the “Squeeze-and-Excitation” (SE) blocks. Secondly, towards the class imbalance problem, we adopt GAN to generate samples of minority classes. Finally, we draw lessons from the curriculum learning strategy by teaching our classifier training from original easy samples to generated complex samples, which improves the classification ability. Experimental results show that our method achieves around 0.5% gains for accuracy and 0.02 gains for F1 score respectively outperforming the state-of-the-art DCNNs.

Plankton classification is becoming critically important as people concentrate more on oceans and global environment changing. Data of plankton species naturally exhibit imbalance in their class distribution. Meanwhile, it arouses fine-grained classification challenge. Although Convolutional Neural Networks (CNNs) have human-level performance on image classification task, they tend to be biased to large classes without considering the imbalance issue. In this paper, we introduce Generative Adversarial Network (GAN) based generative model to overcome these challenges. Our proposed model consists of fully convolutional layers, and includes three parts: a generative model G, a discriminative model D and a classification model C. We train generative and discriminative models on small classes data to learn a discriminative features through D model and reduce mode missing problem to some extent. We implement classification task using shared CNN layers of D model on whole data. Experimental results show that our model significantly improved the F1 score on an imbalanced plankton dataset with well-generated plankton images.

BACKGROUND: Plankton, including phytoplankton and zooplankton, are the main source of food for organisms in the ocean and form the base of marine food chain. As the fundamental components of marine ecosystems, plankton is very sensitive to environment changes, and the study of plankton abundance and distribution is crucial, in order to understand environment changes and protect marine ecosystems. This study was carried out to develop an extensive applicable plankton classification system with high accuracy for the increasing number of various imaging devices. Literature shows that most plankton image classification systems were limited to only one specific imaging device and a relatively narrow taxonomic scope. The real practical system for automatic plankton classification is even non-existent and this study is partly to fill this gap. RESULTS: Inspired by the analysis of literature and development of technology, we focused on the requirements of practical application and proposed an automatic system for plankton image classification combining multiple view features via multiple kernel learning (MKL). For one thing, in order to describe the biomorphic characteristics of plankton more completely and comprehensively, we combined general features with robust features, especially by adding features like Inner-Distance Shape Context for morphological representation. For another, we divided all the features into different types from multiple views and feed them to multiple classifiers instead of only one by combining different kernel matrices computed from different types of features optimally via multiple kernel learning. Moreover, we also applied feature selection method to choose the optimal feature subsets from redundant features for satisfying different datasets from different imaging devices. We implemented our proposed classification system on three different datasets across more than 20 categories from phytoplankton to zooplankton. The experimental results validated that our system outperforms state-of-the-art plankton image classification systems in terms of accuracy and robustness. CONCLUSIONS: This study demonstrated automatic plankton image classification system combining multiple view features using multiple kernel learning. The results indicated that multiple view features combined by NLMKL using three kernel functions (linear, polynomial and Gaussian kernel functions) can describe and use information of features better so that achieve a higher classification accuracy.

Saliency‐based marker‐controlled watershed method was proposed to detect and segment phytoplankton cells from microscopic images of non‐setae species. This method first improved IG saliency detection method by combining saturation feature with colour and luminance feature to detect cells from microscopic images uniformly and then produced effective internal and external markers by removing various specific noises in microscopic images for efficient performance of watershed segmentation automatically. The authors built the first benchmark dataset for cell detection and segmentation, including 240 microscopic images across multiple phytoplankton species with pixel‐wise cell regions labelled by a taxonomist, to evaluate their method. They compared their cell detection method with seven popular saliency detection methods and their cell segmentation method with six commonly used segmentation methods. The quantitative comparison validates that their method performs better on cell detection in terms of robustness and uniformity and cell segmentation in terms of accuracy and completeness. The qualitative results show that their improved saliency detection method can detect and highlight all cells, and the following marker selection scheme can remove the corner noise caused by illumination, the small noise caused by specks, and debris, as well as deal with blurred edges.

In this brief, the concept of logical stochastic resonance is applied to implement the Set-Reset latch in a synthetic gene network derived from a bacteriophage λ . Clear Set-Reset latch operation is obtained when the network is only subjected to periodic forcing. The correct probability of obtaining the desired logic operation first increases to unity and then decreases as the amplitude of the periodic forcing increases. In addition, the output logic operation can be easily morphed by tuning the frequency and the amplitude of the periodic forcing. At the same time, we indicate that adding moderate periodic forcing to the background Gaussian noise may increase the length of the optimal plateau of getting the desired logic operation in genetic regulatory network. We also point out that robust Set-Reset latch operation can be obtained using the interplay of periodic forcing and background noise when the noise strength is lower than what is required.