Software fault localization aims to identify faulty elements in a program by analyzing program information and the execution data of test cases. This process plays a crucial role in improving development efficiency, reducing debugging costs, and ensuring reliable software operation. However, in practical scenarios, due to the large scale of programs and the relatively small proportion of faulty statements, the number of failing test cases in a test suite is lower than that of passing test cases to some extent, leading to an imbalance in defect knowledge. Additionally, mutual interference and masking among multiple faults in a program can introduce characterizing noise into the test suite, further increasing the difficulty of fault localization. To address these limitations, we propose a novel method called SA-BCL, which aims to tackle the issues of defect knowledge imbalance and characterizing noise. SA-BCL comprises four key components: the data processing component, the defect knowledge balancing component, the confident learning denoising component, and the program spectrum reducing component. Specifically, the data processing component constructs the program spectrum by analyzing program behaviors and test results during the execution of test cases. The defect knowledge balancing component mitigates the imbalance of defect knowledge by employing boundary identification and oversampling techniques to augment the spectrum corresponding to failing test cases located at the boundary of the test suite. The confident learning denoising component leverages confident learning techniques to identify and eliminate characterizing noise in the program spectrum. The program spectrum reducing component iteratively computes the suspiciousness scores of program elements and reduces the spectrum to facilitate fault localization. We conduct a series of experiments on both the synthetic multi-fault dataset and the real multi-fault dataset, comparing SA-BCL with baseline approaches. The experimental results clearly demonstrate that SA-BCL outperforms the state-of-the-art methods, achieving improvements of up to 37.5% in average wasted effort, 29.2% in precision, and 22.5% in recall. In addition, SA-BCL maintains comparable time costs within the same order of magnitude as baseline methods, and most of the improvements are statistically significant, demonstrating its robustness.

Multi-modal Knowledge Graph Completion (MMKGC) aims to predict missing entities, relations, or attributes in knowledge graphs by collaboratively modeling the triple structure and multimodal information (e.g., text, images, videos) associated with entities. This approach facilitates the automatic discovery of previously unobserved factual knowledge. However, existing MMKGC methods encounter several critical challenges: (i) the imbalance of inter-entity information across different modalities; (ii) the heterogeneity of intra-entity multimodal information; and (iii) for a given entity, the informational contributions of different modalities are inconsistent across contexts. In this paper, we propose a novel **L**arge model-driven **B**alanced **M**ultimodal **K**nowledge **G**raph **C**ompletion framework, termed LBMKGC. Subsequently, to bridge the semantic gap between heterogeneous modalities, LBMKGC aligns the multimodal embeddings of entities semantically by using the CLIP (Contrastive Language-Image Pre-Training) model. Furthermore, LBMKGC adaptively fuses multimodal embeddings with relational guidance by distinguishing between the perceptual and conceptual attributes of triples. Finally, extensive experiments conducted against 21 state-of-the-art baselines demonstrate that LBMKGC achieves superior performance across diverse datasets and scenarios while maintaining efficiency and generalizability. Our code and data are publicly available at: https://github.com/guoynow/LBMKGC.

The rise in urbanization and the demand for refined oil pose a significant challenge in improving transportation efficiency while ensuring the safety of gas station inventories. Traditional optimization methods, which rely on deterministic models or demand forecasting, enhance efficiency but struggle with uncertainties such as demand fluctuations. Multi-agent reinforcement learning (MARL) presents a promising approach for adaptive cooperative dispatching. This study models the refined oil dispatch problem as a partially observable constrained Markov game (CPOMG), in which agents make cooperative decisions under inventory constraints with limited observation, and proposes Hierarchical Action-Constrained MAPPO (HAC-MAPPO), a novel cooperation MARL algorithm to find the optimal cooperative dispatching policy of the game. Within the CPOMG framework, the HAC-MAPPO agents optimize order generation decisions based on the states of gas station inventories. The subsequent delivery routing for these orders is optimized using an integrated fuel routing tabu search algorithm, which is part of the environment’s state transition logic. HAC-MAPPO incorporates inventory safety thresholds to constrain order generation decisions across fuel types. It employs a dual-critic architecture and a shared hierarchical actor network for efficient decentralized policy learning. Crucially, the Lagrangian relaxation technique is applied to transform the constrained actor optimization objective (for order generation) into an unconstrained form. Evaluations carried out in real-world scenarios (based on actual geographic coordinates) and synthetic scenarios using multiple types of synthetic fuel consumption datasets demonstrate that the proposed model and algorithm significantly reduce inventory violations and improve dispatch efficiency and safety under varying demand patterns, outperforming baseline methods.

Multi-agent Reinforcement Learning (MARL) has shown considerable promise in enhancing the efficiency of adaptive traffic signal control (ATSC) systems. However, existing MARL approaches primarily focus on optimizing overall traffic flow, often overlooking the issue of fairness in vehicle waiting times. Considering that there is no need to strive for the ultimate fairness, this paper models the ATSC problem as a Constrained Partially Observable Markov Game (CPOMG), where fairness is modeled as a constraint on the maximum waiting time of vehicles on lanes of intersections instead of a reward term that pursues maximization. CPOMG aims to find a cooperative control policy with optimal traffic efficiency within the constrained solution space by multiple agents. On this basis, this paper proposes a new centralized training and decentralized execution cooperative MARL method, i.e., vehicle-level fairness multi-agent proximity policy optimization (VF-MAPPO). VF-MAPPO leverages a centralized trained global Critic Network to estimate the average vehicle traffic efficiency and vehicle maximum waiting time, and an Actor Network shared by all intersections for decentralized execution, which converts the optimization problem with constraints to an unconstrained optimization objective through the Lagrange multiplier method and adopts proximity policy optimization during training. Additionally, VF-MAPPO incorporates spatial-temporal graph attention in the Critic network to efficiently extract state representations in multi-intersection environments. We qualitatively analyzed the monotonic improvement guarantee of VF-MAPPO. Extensive experimental validation across two real-world and one synthetic scenarios substantiates that VF-MAPPO enhances vehicle-level fairness and maintains average traffic efficiency, surpassing state-of-the-art methods.

Existing multi-agent reinforcement learning (MARL) in adaptive traffic signal control (ATSC) typically models cooperative control of multiple intersections as a cooperative Markov game, optimizing the average traffic efficiency of intersections with the same emphasis. However, it is insufficient to meet the requirements in real ATSC scenarios when all intersections are treated equally. To this end, this work proposes the Captain-Member Markov Game (CM-MG) that considers the different priorities between intersections. CM-MG categorizes intersections into special and ordinary intersections, controlled by captain agents and member agents to optimize the traffic efficiency of local and overall road networks, respectively. The cooperative requirements of CM-MG are achieved through a sequential decision-making principle. Captains have priority in choosing actions, and members make decisions sequentially, following the breadth-first traversal order in the road network after obtaining their precursors’ intentions. Then, a cooperative MARL algorithm, i.e, Sequential Decision Deep Graph Network (GNSD-Light), is proposed to learn the optimal joint policy that meets the learning goals of both captains and members. To be unrestricted by the scales of intersections, GNSD-Light adopts an autoregressive framework where all agents make decisions sequentially in a predetermined order by sharing the same decision model. In addition, to obtain sufficient state representation, two relative position encoding-based spatiotemporal representation modules are designed for GNSD-Light based on the characteristics of ATSC scenarios. Finally, through adequate experiments and qualitative analysis, we have confirmed that our method effectively balances traffic efficiency among both the overall road network and special intersections.

Abstract Key management is an important security service for protecting wireless sensor networks (WSNs). Among various existing schemes, exclusion basis system (EBS) is a practical solution that can be easily implemented to provide long‐term and flexible protection for WSNs. The involved rekeying strategy in EBS can efficiently evict the compromised node and update the key system. However, the relatively small key pool leads to high correlation among the generated key rings. Consequently, it is almost impossible for EBS‐based schemes to efficiently defend collusion attack with their rekeying mechanisms. In this paper, we first analyze the impact of collusion attack on WSNs, especially that in the case where the keys of the compromised nodes can form a connected graph. Then, we propose a novel key management scheme based on EBS. The proposed scheme is termed ast‐EEBS because it can effectively resist the referred collusion attack formed byt(t > 1) nodes. Furthermore, we assume that the proposed scheme is implemented in hierarchical WSNs. In this case, two layers oft‐EEBS administrative keys are used. The upper layer is implemented among the base station and all cluster leaders, while the lower layer involves at‐EEBS for each cluster. The results of performance evaluation show that the proposed scheme has better resistance to collusion attack than other schemes. Therefore, the proposed scheme can provide better security service for WSNs. Copyright © 2017 John Wiley & Sons, Ltd.