Xiaowei Li

Fine-grained poisonous mushroom recognition in natural scenes is challenging because of complex backgrounds, subtle morphological differences, and the limited interpretability of model decisions. To address these challenges, this paper proposes Att-FiLM, an attention-enhanced multi-scale Feature-Wise Linear Modulation network for poisonous mushroom image recognition. The model adopts an asymmetric dual-backbone architecture in which a frozen ConvNeXt-Base branch provides global semantic priors, while a trainable EfficientNet-B0 branch learns local discriminative features. Rather than directly concatenating heterogeneous features, Att-FiLM generates scale and shift parameters from semantic features and performs channel-wise modulation on multi-scale EfficientNet features at Stage 2 and Stage 4. This mechanism enables global semantic information to guide local feature learning while reducing feature redundancy and semantic inconsistency. Experimental results show that Att-FiLM achieves an Accuracy of 95.58% and an F1-score of 0.9455 on the poisonous/edible binary classification task. On the 190-class species-level classification task, it achieves a Top-1 Accuracy of 93.63% and a Macro-F1 of 0.9347. Interpretability analysis further shows that decision-relevant responses are frequently associated with morphologically relevant regions, including gills, annuli, volvae, and cap textures. These results indicate that Att-FiLM provides effective recognition performance together with interpretable decision evidence for mushroom recognition in complex natural scenes.

Camera traps are widely used in wildlife surveys but often generate numerous empty images due to false triggers, which are time-consuming to filter manually. Microsoft’s MegaDetector (MD) is commonly applied for empty image filtering; however, setting its confidence threshold is challenging because a high threshold increases the false negative rate (FNR), whereas a low threshold raises the false positive rate (FPR), making it difficult to achieve a satisfactory trade-off between the two. To address this dilemma, we proposed three MD-based methods with different performance and computational requirements. For FNR-sensitive users, we proposed MD_CS, which integrated context image similarity to achieve a very low FNR while controlling the FPR. For FPR-sensitive users, we proposed MD_CL, which used MD to generate supervisory signals for training a classification model, thereby reducing the FPR while controlling the FNR. For users requiring both low FNR and low FPR, we proposed MD_CS_CL, an ensemble method that combined the strengths of MD_CS and MD_CL. Experimental results on multiple datasets showed that these three methods achieved a significantly better balance between FNR and FPR compared to the threshold adjustment method of the MD method. Specifically, MD_CS maintained an FNR of 1.8–5.9% while controlling the FPR at 4.9–8.5%; MD_CL kept the FPR at 0.4–2.6% while controlling the FNR at 3.8–8.4%; MD_CS_CL achieved an optimal balance with an FNR of 1.4–4.0% and an FPR of 0.3–0.9%. On a server equipped with an RTX 4090 GPU, the three methods processed 6.7, 5.4, and 4.0 images per second, respectively, and none required manual annotation. Consequently, the proposed methods reduce labor costs, improve work efficiency, and provide reliable technical support for large-scale wildlife monitoring by achieving a superior FNR–FPR trade-off compared to MD’s traditional threshold regulation.

Software vulnerability mining is important for improving software reliability and security. Compared with sequence-based models, graph neural networks (GNNs) can explicitly model program structures such as syntax, control flow, data flow, call relations, and dependency paths, and have therefore been widely studied for vulnerability detection, localization, explanation, and repair. This paper presents a PRISMA-informed structured topical review of 87 studies and addresses five research questions concerning program graph representation, homogeneous and heterogeneous GNN architectures, Graph–LLM integration, evaluation reliability, and future research directions. The reviewed evidence shows that graph-based methods are most effective when vulnerability mechanisms can be faithfully represented through explicit structural relations. However, their reported performance remains strongly affected by duplicated samples, random function-level splits, noisy labels, incomplete repository context, graph-construction errors, and weak explanation protocols. Homogeneous GNNs provide efficient structural message passing but may mix different semantic relations, whereas heterogeneous GNNs preserve relation types more explicitly at the cost of greater graph-quality and computational requirements. Graph–LLM systems can improve semantic reasoning, repository-level analysis, explanation generation, and repair support, but their benefits should be evaluated together with memory consumption, inference latency, deployment complexity, and verification cost. This review further proposes minimum requirements for reliable vulnerability benchmarks and verifiable explanations, and develops a strategic agenda covering leakage-resistant datasets, uncertainty-aware graph construction, repository-level evaluation, cost-effective Graph–LLM collaboration, and graph-guided autonomous vulnerability repair.

The “sharing” feature provided by smart home platforms enables multiple users to access the device simultaneously with different roles and permissions, but it also presents new security challenges for the design and implementation of the permission management. The key issue is that the platform adopts two different permission assignments on the app side and the cloud side, and these two assignments must maintain consistency in authorizing. Unfortunately, real-world smart home platforms may not be able to ensure this when implementing RBAC (Role-Based Access Control) model. The inconsistency between these assignments can lead to security vulnerabilities, which can be easily exploited by malicious users. Although many existing studies have revealed security issues with smart home platforms, less attention has been paid to the sharing feature and permission assignments, as well as security issues that arise from this. In this work, we conducted a systematic study on the RBAC model and permission management of smart home platforms. To overcome technical challenges imposed by the “black-box” platform, we also proposed a novel testing framework. By testing 10 smart home platforms that all belong to the “device-connected, black-box, and multi-user supported” category, we collected each platform's “configurable permission assignment” and inferred “enforced permission assignment”. At last, we identified 44 inconsistencies that could lead to security vulnerabilities. Malicious users could exploit these vulnerabilities to initiate attacks such as device hijacking, unauthorized access, illegal control, and eavesdropping. We promptly reported these vulnerabili

To autonomously detect wildlife images captured by camera traps on a platform with limited resources and address challenges such as filtering out photos without optimal objects, as well as classifying and localizing species in photos with objects, we introduce a specialized wildlife object detector tailored for camera traps. This detector is developed using a dataset acquired by the Saola Working Group (SWG) through camera traps deployed in Vietnam and Laos. Utilizing the YOLOv6-N object detection algorithm as its foundation, the detector is enhanced by a tailored optimizer for improved model performance. We deliberately introduce asymmetric convolutional branches to enhance the feature characterization capability of the Backbone network. Additionally, we streamline the Neck and use CIoU loss to improve detection performance. For quantitative deployment, we refine the RepOptimizer to train a pure VGG-style network. Experimental results demonstrate that our proposed method empowers the model to achieve an 88.3% detection accuracy on the wildlife dataset in this paper. This accuracy is 3.1% higher than YOLOv6-N, and surpasses YOLOv7-T and YOLOv8-N by 5.5% and 2.8%, respectively. The model consistently maintains its detection performance even after quantization to the INT8 precision, achieving an inference speed of only 6.15 ms for a single image on the NVIDIA Jetson Xavier NX device. The improvements we introduce excel in tasks related to wildlife image recognition and object localization captured by camera traps, providing practical solutions to enhance wildlife monitoring and facilitate efficient data acquisition. Our current work represents a significant stride toward a fully automated animal observation system in real-time in-field applications.

In today’s interconnected world, identity authentication and key agreement are important links in the secure communication process of IoT terminal devices. In the edge computing environment, with the frequent cross-domain authentication and data sharing of IoT devices in different security domains, identity authentication faces a series of challenges and security issues. Most of the traditional identity authentication methods are based on public key infrastructure, which is prone to single point of failure and is not applicable to the distributed architecture of edge computing. In this article, we apply blockchain technology to the identity authentication and key agreement process of IoT terminal devices. In order to meet cross-domain requests from terminal devices in different security domains, a multi-layer blockchain authentication architecture is designed. The hash value of the digital certificate is stored on the blockchain and combined with dynamic accumulator technology to enhance the reliability and authentication efficiency of the digital certificate. Security analysis and experimental results demonstrate that our scheme can achieve efficient and secure authentication and key agreement.

Spatial high utility co-location patterns mining takes pattern utility participation index as interest measure, but it still lacks standard method for calculating pattern utility participation index. This paper presents some reasonable definitions of spatial high utility colocation patterns based on the datasets of instance with utility value, such as Feature Actual Participation Weight, Feature Utility Participation Index and Pattern Utility Participation Index and many more. A basic algorithm BWHMA(high utility co-location patterns mining based on feature actual participation weight) is proposed. For improving the efficiency of algorithm BWHMA, one pruning algorithm FUPRA(based on feature utility participation rate) and one optimization algorithm FRPWA(based on feature actual participation weight) are put forward. Finally, a large number of comparative experiments have been done on both synthetic and real datasets. The experiments show that the proposed algorithms are feasible, correct and valid.

Password-based authenticated key exchange (PAKE) protocol has been widely used in practice since it is convenient for users. However, the easy-to-remember property of the password also brings security problem. In this letter, we show there is an off-line dictionary attack in an efficient PAKE protocol when the smart card is lost. In order to resist the attack we give a countermeasure to improve it. The countermeasure makes a simple change to the original protocol which does not affect the efficiency of the protocol.