The evidential reasoning (ER) rule framework has been widely applied in multi-attribute decision analysis and system assessment to manage uncertainty. However, traditional ER implementations rely on two critical limitations: 1) unrealistic assumptions of complete evidence independence, and 2) a lack of mechanisms to differentiate causal relationships from spurious correlations. Existing similarity-based approaches often misinterpret interdependent evidence, leading to unreliable decision outcomes. To address these gaps, this study proposes a causality-enhanced ER rule (CER-e) framework with three key methodological innovations: 1) a multidimensional causal representation of evidence to capture dependency structures; 2) probabilistic quantification of causal strength using transfer entropy, a model-free information-theoretic measure; 3) systematic integration of causal parameters into the ER inference process while maintaining evidential objectivity. The PC algorithm is employed during causal discovery to eliminate spurious correlations, ensuring robust causal inference. Case studies in two types of domains—telecommunications network security assessment and structural risk evaluation—validate CER-e’s effectiveness in real-world scenarios. Under simulated incomplete information conditions, the framework demonstrates superior algorithmic robustness compared to traditional ER. Comparative analyses show that CER-e significantly improves both the interpretability of causal relationships and the reliability of assessment results, establishing a novel paradigm for integrating causal inference with evidential reasoning in complex system evaluation.

Dynamic publishing of social network graphs offers insights into user behavior but brings privacy risks, notably re-identification attacks on evolving data snapshots. Existing methods based on $k$-anonymity can mitigate these attacks but are cumbersome, neglect dynamic protection of community structure, and lack precise utility measures. To address these challenges, we present a dynamic social network graph anonymity scheme with community structure protection (DSNGA-CSP), which achieves the dynamic anonymization process by incorporating community detection. First, DSNGA-CSP categorizes communities of the original graph into three types at each timestamp, and only partitions community subgraphs for a specific category at each updated timestamp. Then, DSNGA-CSP achieves intra-community and inter-community anonymization separately to retain more of the community structure of the original graph at each timestamp. It anonymizes community subgraphs by the proposed novel $k$-composition method and anonymizes inter-community edges by edge isomorphism. Finally, a novel information loss metric is introduced in DSNGA-CSP to precisely capture the utility of the anonymized graph through original information preservation and anonymous information changes. Extensive experiments conducted on five real-world datasets demonstrate that DSNGA-CSP consistently outperforms existing methods, providing a more effective balance between privacy and utility. Specifically, DSNGA-CSP shows an average utility improvement of approximately 30% compared to TAKG and CTKGA for three dynamic graph datasets, according to the proposed information loss metric IL.

Reinforcement learning as autonomous learning is greatly driving artificial intelligence (AI) development to practical applications. Having demonstrated the potential to significantly improve synchronously parallel learning, the parallel computing based asynchronous advantage actor-critic (A3C) opens a new door for reinforcement learning. Unfortunately, the acceleration's inuence on A3C robustness has been largely overlooked. In this paper, we perform the first robustness assessment of A3C based on parallel computing. By perceiving the policy’s action, we construct a global matrix of action probability deviation and define two novel measures of skewness and sparseness to form an integral robustness measure. Based on such static assessment, we then develop a dynamic robustness assessing algorithm through situational whole-space state sampling of changing episodes. Extensive experiments with different combinations of agent number and learning rate are implemented on an A3C-based pathfinding application, demonstrating that our proposed robustness assessment can effectively measure the robustness of A3C, which can achieve an accuracy of 83.3%.