Session-based recommendation systems focus on capturing users’ evolving intents from short interaction sequences, yet they persistently face three key challenges: the difficulty in dynamically discriminating between short-term and long-term interests, the inherent tradeoff between sequential modeling and relational dependency learning, and the pervasive noise and sparsity in real-world session data. To tackle these issues, we propose Multivariate Relationship Graph Embedding (MRGE), a novel framework that synergizes enhanced recurrent modeling with graph-structured representations. Specifically, MRGE leverages a self-attention–enhanced RNN to concurrently model short-term intents and long-term preferences within sessions, while constructing a heterogeneous session graph that captures multi-relational item dependencies without compromising temporal fidelity. In addition, we introduce an auxiliary edge augmentation mechanism based on neighbor similarity to mitigate data sparsity and noise, thereby facilitating more robust information propagation. Extensive experiments on three public benchmarks— Delicious , Gowalla , and Foursquare —show that MRGE consistently surpasses state-of-the-art baselines and achieves significant improvements in top- \(K\) recommendation accuracy. Our implementation is available at: https://github.com/July-jz/MRGEcode .

Reliable circuits help prevent artificial intelligence (AI) systems from being corrupted by the soft errors occurred in memories or combinational circuits, which promotes the development of AI security. However, it is a great challenge to measure the reliability of combinational circuits at register transfer level (RTL) rapidly and efficiently. In this paper, a new fast and accurate computational model based on stochastic computation (SC) is presented to meet these objectives. In the proposed approach, the circuit netlists at RTL are parsed to satisfy the requirements of SC on the bitstream structure of the circuits, and then a Sobol sequence-based algorithm for generating uniform non-Bernoulli sequences is built to reduce the random fluctuations occurred in probability calculations. After that, an adaptive algorithm based on a MAX–MIN ant system is constructed using graphics processing unit-based parallel schemes to greatly accelerate the calculation. The experimental results validate our proposed technique, showing that this approach was approximately 51 and 42 times faster than the traditional SC approach and the stochastic computational model (SCM), respectively; its required sequence length was approximately 1.66 times shorter than that of the traditional SC approach, and its relative error was two times smaller than that of the SCM.