Generating anomalies is a crucial method to enhance detection and classification performance by expanding anomalous data repository. However, existing anomaly generation methods overlook the intrinsic entanglement between diverse anomaly types and product structures, leading to semantic ambiguity. We propose CADiff, a context-aware generation framework that reframes anomalies as compositional perturbations. Firstly, we propose Context-aware Text Prompt (CTP), a mechanism which contains multiple tokens that characterize anomalies and products separately to enhance the contextual consistency of generated images and refine the local variability of anomalies. Secondly, we develop Self-adaptive Spatial Control (SSC), a self-adaptive interaction design that mitigates anomaly leakage or missing phenomena. Thirdly, we introduce Intensity-controllable Attention Re-weighting (IAR), an inference scheduling scheme with the ability to amplify or attenuate abnormal semantic effects to improve generation diversity. Extensive experiments on MVTec AD and VisA datasets demonstrate the superiority of our proposed method over state-of-the-art methods in both realism and diversity of the generated results, and significantly improve the performance of downstream tasks, including anomaly detection, anomaly localization, and anomaly classification tasks.

Affective Forecasting is an psychology task that involves predicting an individual's future emotional responses, often hampered by reliance on external factors leading to inaccuracies, and typically remains at a qualitative analysis stage. To address these challenges, we narrows the scope of Affective Forecasting by introducing the concept of Human-interaction-based Emotion Forecasting (EF). This task is set within the context of a two-party interaction, positing that an individual's emotions are significantly influenced by their interaction partner's emotional expressions and informational cues. This dynamic provides a structured perspective for exploring the patterns of emotional change, thereby enhancing the feasibility of emotion forecasting.

Dynamic Facial Expression Recognition (DFER) is crucial for affective computing but often overlooks the impact of scene context. We have identified a significant issue in current DFER tasks: human annotators typically integrate emotions from various angles, including environmental cues and body language, whereas existing DFER methods tend to consider the scene as noise that needs to be filtered out, focusing solely on facial information. We refer to this as the Rigid Cognitive Problem. The Rigid Cognitive Problem can lead to discrepancies between the cognition of annotators and models in some samples. To align more closely with the human cognitive paradigm of emotions, we propose an Overall Understanding of the Scene DFER method (OUS). OUS effectively integrates scene and facial features, combining scene-specific emotional knowledge for DFER. Extensive experiments on the two largest datasets in the DFER field, DFEW and FERV39k, demonstrate that OUS significantly outperforms existing methods. By analyzing the Rigid Cognitive Problem, OUS successfully understands the complex relationship between scene context and emotional expression, closely aligning with human emotional understanding in real-world scenarios.

The current advancements in Dynamic Facial Expression Recognition (DFER) methods mainly focus on better capturing the spatial and temporal features of facial expressions. However, DFER datasets contain a substantial amount of noisy samples, and few have addressed the issue of handling this noise. We identified two types of noise: one is caused by low-quality data resulting from factors such as occlusion, dim lighting, and blurriness; the other arises from mislabeled data due to annotation bias by annotators. Addressing the two types of noise, we have meticulously crafted a Dynamic Dual-Stage Purification (D2SP) Framework. This initiative aims to dynamically purify the DFER datasets of these two types of noise, ensuring that only high-quality and correctly labeled data is used in the training process. To mitigate low-quality samples, we introduce the Coarse-Grained Pruning (CGP) stage, which computes sample weights and prunes those low-weight samples. After CGP, the Fine-Grained Correction (FGC) stage evaluates prediction stability to correct mislabeled data. Moreover, D2SP is conceived as a general, plug-and-play framework, tailored to integrate seamlessly with prevailing DFER methods. Extensive experiments covering prevalent DFER datasets and deploying multiple benchmark methods have substantiated D2SP’s ability to enhance performance metrics.

Anomaly detection is critical in industrial manufacturing for ensuring product quality and improving efficiency in automated processes. The scarcity of anomalous samples limits traditional detection methods, making anomaly generation essential for expanding the data repository. However, recent generative models often produce unrealistic anomalies increasing false positives, or require real-world anomaly samples for training. In this work, we treat anomaly generation as a compositional problem and propose ComGEN, a component-aware and unsupervised framework that addresses the gap in logical anomaly generation. Our method comprises a multi-component learning strategy to disentangle visual components, followed by subsequent generation editing procedures. Disentangled text-to-component pairs, revealing intrinsic logical constraints, conduct attention-guided residual mapping and model training with iteratively matched references across multiple scales. Experiments on the MVTecLOCO dataset confirm the efficacy of ComGEN, achieving the best AUROC score of$\mathbf{9 1. 2 \%}$. Additional experiments on the real-world scenario of Diesel Engine and widelyused MVTecAD dataset demonstrate significant performance improvements when integrating simulated anomalies generated by ComGEN into automated production workflows.

Permissioned blockchains have found widespread adoption across diverse scenarios, ensuring data authenticity and integrity. However, transaction conflicts, as an inherent performance challenge in permissioned blockchains, can significantly decrease system throughput and thus degrade its Quality of Service (QoS) under substantial transaction contention. Existing approaches mitigate conflicts typically by either aborting or blocking transactions in advance, encountering two main issues: (i) resource wastage due to transaction failure and (ii) performance degradation, particularly under large block sizes or high transaction contention. In this paper, we propose PreFabric, a novel permissioned blockchain framework that guarantees high throughput by resolving the transaction conflict problem. We first conduct a comprehensive analysis of the transaction scenarios preceding simulation execution of the endorsing phase in the blockchain system to identify potential conflict-causing situations. Then, we devise an key-locking method to prevent transaction conflicts and propose concurrency control strategies based on dependency analysis, encompassing a transaction merging mechanism, an key-renaming mechanism and concurrent validating mechanisms, to improve system throughput. The experimental results demonstrate the superior performance of our method over state-of-the-art methods, with 2.1× higher effective throughput and 0.48× lower latency.

The rapid spread of multimodal misinformation on social media has raised growing concerns, while research on video misinformation detection remains limited due to the lack of large-scale, diverse datasets. Existing methods often overfit to rigid templates and lack deep reasoning over deceptive content. To address these challenges, we introduce FakeVV, a large-scale benchmark comprising over 100,000 video-text pairs with fine-grained, interpretable annotations. In addition, we further propose Fact-R1, a novel framework that integrates deep reasoning with collaborative rule-based reinforcement learning. Fact-R1 is trained through a three-stage process: (1) misinformation long-Chain-of-Thought (CoT) instruction tuning, (2) preference alignment via Direct Preference Optimization (DPO), and (3) Group Relative Policy Optimization (GRPO) using a novel verifiable reward function. This enables Fact-R1 to exhibit emergent reasoning behaviors comparable to those observed in advanced text-based reinforcement learning systems, but in the more complex multimodal misinformation setting. Our work establishes a new paradigm for misinformation detection, bridging large-scale video understanding, reasoning-guided alignment, and interpretable verification.

Smart contracts are one of the most successful applications of blockchain technology, providing the foundation for a wide range of real-world blockchain applications and occupying a crucial position within the blockchain ecosystem. Hyperledger Fabric, as a influential permissoned blockchain system, warrants in-depth research into the security of its smart contracts. This paper begins by elucidating the sources of security threats to Hyperledger Fabric smart contracts, detailing specific security threats in terms of language inconsistency, external inconsistency, read-write logic, and system security. Subsequently, it provides an overview of research progress in smart contract vulnerability detection techniques, covering feature code matching, symbolic execution, fuzz testing, and intermediate code representation. Based on existing work, it summarizes current methods for detection and evaluation. Finally, drawing upon the summary of existing research efforts, it discusses the challenges faced and potential avenues for future research in the domain of Hyperledger Fabric smart contract vulnerability detection.Related research also facilitates the extension to other heterogeneous permissioned blockchains.

During the 14th Five-Year Plan period, China's urban rail transit market has exhibited steady growth, paralleled by increases in passenger volume and emerging safety challenges. The advent of national standards such as GB 51151 has heightened safety requirements, pressing the need for technological advancements in rail transit security systems. Traditional security systems suffer from isolated operations and inefficient information exchanges, necessitating additional human resources for management. We propose integrating blockchain technology to enhance trust and security across disparate systems. Additionally, the introduction of heterogeneous query blockchain middle-ware facilitates cross-chain data interoperability and advanced querying capabilities, further enriching our multimodal, fine-grained blockchain security management system that leverages Fabric's channel isolation for secondary permission control. This system not only ensures secure data transmission and storage but also addresses privacy and trust issues, enabling unified data handling and traceability across rail transit security platforms. The experiment demonstrated the efficacy of our work

The increasing prevalence of blockchain technology has drawn significant attention to the need for effective Quality of Service (QoS) management in blockchain service provision. In this context, the online tuning of system configurations is pivotal for automatic blockchain services to meet QoS requirements. Past studies on configuration tuning have primarily focused on system adaptability to hardware and network environments, overlooking the dynamic nature of the highly diverse workloads, thus resulting in suboptimal system performance. This paper presents TuneChain, an online configuration auto-tuning approach for permissioned blockchain systems, which addresses the limitations of current methods, particularly in handling dynamic workloads while minimizing tuning costs. TuneChain leverages a Conflict Emergency Mechanism (CF-EM) to mitigate the impact of transaction conflicts on effective throughput and employs the Proximal Policy Optimization (PPO) algorithm coupled with a multi-instance mechanism to offer adaptive configuration recommendations tailored to diverse workloads. Additionally, TuneChain incorporates a Tuning Causal Model (TCModel) based on expert knowledge to guide decision-making in configuration tuning, thereby reducing unnecessary exploration and improving efficiency. Extensive evaluations demonstrate that TuneChain outperforms state-of-the-art approaches to configuration tuning in adapting to dynamic workloads, showcasing its efficacy in enhancing blockchain service performance.

In the field of affective computing, fully leveraging information from a variety of sensory modalities is essential for the comprehensive understanding and processing of human emotions. Inspired by the process through which the human brain handles emotions and the theory of cross-modal plasticity, we propose UMBEnet, a brain-like unified modal affective processing network. The primary design of UMBEnet includes a Dual-Stream (DS) structure that fuses inherent prompts with a Prompt Pool and a Sparse Feature Fusion (SFF) module. The design of the Prompt Pool is aimed at integrating information from different modalities, while inherent prompts are intended to enhance the system's predictive guidance capabilities and effectively manage knowledge related to emotion classification. Moreover, considering the sparsity of effective information across different modalities, the SSF module aims to make full use of all available sensory data through the sparse integration of modality fusion prompts and inherent prompts, maintaining high adaptability and sensitivity to complex emotional states. Extensive experiments on the largest benchmark datasets in the Dynamic Facial Expression Recognition (DFER) field, including DFEW, FERV39k, and MAFW, have proven that UMBEnet consistently outperforms the current state-of-the-art methods. Notably, in scenarios of Modality Missingness and multimodal contexts, UMBEnet significantly surpasses the leading current methods, demonstrating outstanding performance and adaptability in tasks that involve complex emotional understanding with rich multimodal information. Code can be obtained at https://github.com/Xinji-Mai/UMBEnet.

The Janus Problem is a common issue in SDS-based text-to-3D methods. Due to view encoding approach and 2D diffusion prior guidance, the 3D representation model tends to learn content with higher certainty from each perspective, leading to view inconsistency. In this work, we first model and analyze the problem, visualizing the specific causes of the Janus Problem, which are associated with discrete view encoding and shared priors in 2D lifting. Based on this, we further propose the LCGen method, which guides text-to-3D to obtain different priors with different certainty from various viewpoints, aiding in view-consistent generation. Experiments have proven that our LCGen method can be directly applied to different SDS-based text-to-3D methods, alleviating the Janus Problem without introducing additional information, increasing excessive training burden, or compromising the generation effect.

Intelligent detection of product surface defects in the industrial scene is the key to ensuring product quality. On general benchmarks, current unsupervised anomaly detection techniques have achieved significant success. When used in complex industrial environments (e.g., large industrial components with small defects), the model needs to be able to adapt to different imaging scenarios (e.g., illumination and resolution) and accurately detect and localize anomalies, but its performance is still far from satisfactory. Besides, the complex and unstable optical lighting environment for collecting such data poses major challenges in establishing unified benchmarks for optical lighting and imaging resolution in defect detection. To fill this gap, we build a standard imaging system-based multiscene unsupervised anomaly detection dataset, coined as MSC-AD. In particular, it provides 12 imaging scenes, i.e., a cross combination of low-to-high three illuminations and 150 × 150 to 600 × 600 four resolutions, in which six types of large casting surfaces with different structures include five kinds of small defects with sample-level and pixel-level precise ground truth. We systematically investigate representative baseline methods and empirical analysis on this dataset to obtain a number of interesting findings, e.g., how to detach from distinctly different imaging scenes, and how to distinguish between subtly normal–anomaly classes. To the best of our knowledge, MSC-AD is the first multi-illumination, multiresolution, multisurface, and multidefect dataset built in a standard imaging system.

Vertical Federated Learning (VFL) facilitates collaboration on model training among multiple parties, each owning partitioned features of the distributed dataset. Although backdoor attacks have been found as one of the main threats to FL security, research on backdoor attacks in VFL is still in the infant stage. Existing methods for VFL backdoor attacks rely on predicting sample pseudo-labels using approaches such as label inference, which require substantial additional information not readily available in practical FL scenarios. To evaluate the practical vulnerability of VFL to backdoor attacks, we present a target-efficient clean backdoor (TECB) attack for VFL. The TECB approach consists of two phases – i) Clean Backdoor Poisoning (CBP) and Target Gradient Alignment (TGA). In the CBP phase, the adversary trains a backdoor trigger and poisons the model during VFL training. The poisoned model is further fine-tuned in the TGA phase to enhance its efficacy in complex multi-classification tasks. Compared to the existing methods, the proposed TECB achieves a highly effective backdoor attack with very limited information about the target class samples, which is more practical in typical VFL settings. Experimental results verify the superior performance of TECB, achieving above 97% attack success rate (ASR) on three widely used datasets (CIFAR10, CIFAR100, and CINIC-10) with only 0.1% of target labels known, which outperforms the state-of-the-art attack methods. This study uncovers the potential backdoor risks in VFL, enabling the development of secure VFL applications in areas like finance, healthcare, and beyond. Source code is available at: https://github.com/13thDayOLunarMay/TECB-attack

The acquisition and processing of visual data in industrial environments are of paramount importance. High-resolution (HR) images offer superior clarity and richer textural detail compared to low-resolution (LR) images. On the one hand, owing to the incorporation of richer information, HR images demonstrate substantially enhanced performance compared to LR images in downstream applications, such as anomaly detection. On the other hand, they provide valuable insights to designers and quality inspectors who require a detailed understanding of the images. Currently, the majority of research on super-resolution focuses on natural scenes such as cities and fields, however, the development of datasets for industrial scenes is still in its infancy. To address the image distortion in building realistic LR-HR image pairs in the industry environment, we design a capture to registration framework. It consists of the standard imaging system, physical calibration of the imaging system, as well as the rigid to elastic registration of the LR-HR image pairs. Thus, we build the first realistic industrial sence super-resolution dataset (IndSR), comprises of 50 sets of calibrated images with three scale factors and five typical defects. To benchmark IndSR, we employ quantitative, qualitative, and task-oriented studies to evaluate the representative super-resolution and anomaly detection methods. Besides, we systematically investigate and discuss the performances and results of the existing SISR methods to advance research in the field of super-resolution in industry environment. The IndSR dataset can be available from https://byw4ng.github.io/IndSR/.

Federated learning (FL) offers a promising approach to efficient machine learning with privacy protection in distributed environments, such as Internet of Things (IoT) and mobile-edge computing (MEC). The effectiveness of FL relies on a group of participant nodes that contribute their data and computing capacities to the collaborative training of a global model. Therefore, preventing malicious nodes from adversely affecting the model training while incentivizing credible nodes to contribute to the learning process plays a crucial role in enhancing FL security and performance. Seeking to contribute to the literature, we propose a blockchain-empowered secure and incentive FL (BESIFL) paradigm in this article. Specifically, BESIFL leverages blockchain to achieve a fully decentralized FL system, where effective mechanisms for malicious node detections and incentive management are fully integrated in a unified framework. The experimental results show that the proposed BESIFL is effective in improving FL performance through its protection against malicious nodes, incentive management, and selection of credible nodes.

In recent years, the commodities industry has grown rapidly under the stimulus of domestic demand and the expansion of cross-border trade. It has also been combined with the rapid development of e-commerce technology in the same period to form a flexible and efficient e-commerce system for bulk commodities. However, the hasty combination of both has inspired a lack of effective regulatory measures in the bulk industry, leading to constant industry chaos. Among them, the problem of lagging evidence in regulatory platforms is particularly prominent. Based on this, we design a blockchain-based evidential and secure bulk-commodity supervisory system (abbr. BeBus). Setting different privacy protection policies for each participant in the system, the solution ensures effective forensics and tamper-proof evidence to meet the needs of the bulk business scenario.