Dynamic ridesharing has garnered significant attention in recent years due to its numerous benefits. Existing ridesharing algorithms often employ a “filter-and-refine” frame-work, where a large set of candidate vehicles is initially selected for each ride order, followed by computationally intensive route planning for each candidate. However, this process can lead to significant response delays and limit system efficiency. To address this challenge, we propose an order-vehicle pre-matching recommendation approach (PreMR) that refines the candidate set before route planning. PreMR leverages spatial-temporal intervals and a self-attention mechanism to encode diverse order and vehicle information into uniform and informative representations, enabling it to accurately identify the most suitable vehicles for each order. Extensive experiments using real-world datasets and four representative ridesharing algorithms demonstrate that PreMR significantly reduces order response time (by 46.78% on average) while maintaining high service quality, with a slight trade-off in the order completion rate.

To select appropriate web services for users, the Quality-of-Service (QoS) based collaborative prediction models are widely used. Despite the success of collaborative prediction models in selecting appropriate web services for users, existing models do not take into account the users' authority to manage their own generated data as stipulated by many privacy-preserving regulations, such as the General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA). Moreover, unlearning is urgently needed due to the security concerns such as data poisoning attacks. Existing QoS prediction methods are not optimized for unlearning, suffering from low model availability when handling unlearning requests by full re-training. To address this problem, we propose QoSEraser: a novel efficient machine unlearning framework for QoS prediction tasks. The central concepts of the QoSEraser involve: (1) dividing the training data into multiple shards to train submodels according to the cluster results on graph embeddings induced by random walk on contextual information graph. Such division ensures the preservation of collaborative signals in collected QoS records; (2) a concatenate aggregation method and a stacking & attention-based aggregation method are used to condense information in fragmented embeddings to a uniform one adaptively. Experiments on large-scale datasets show that QoSEraser achieves efficient forgetting learning and outperforms state-of-the-art unlearning approaches in terms of performance. Source codes are available at https://github.com/ZengYuXiang7/QoSEraser.

Quality-of-Service (QoS) based collaborative prediction models are emerging to select appropriate edge cloud services for users. Nevertheless, there are still challenges in the realworld QoS prediction task. First, existing QoS prediction models are mostly transductive, failing to generalize to unseen users and services. Secondly, an accurate prediction model remains unexplored under the extreme sparse data scenario, where only a few interactions are available for collaborative filtering. To address these problems, we propose -Inductive -Subgraph -Pattern -Aware Graph Neural Network (ISPA-GNN), which leverages a novel graph-based collaborative filtering method with a subgraph sampling strategy. We further optimize the embeddings components, replacing the user/service embeddings with compositional context information to enable better generalization to unseen nodes while reducing memory usage. Extensive experiments on a large-scale real-world service QoS dataset demonstrate some decent properties of our model, including high prediction accuracy, memory efficiency, and slight performance degradation even if 25% of users/services are never seen.

Reversible Boolean function (RBF) is a one-to-one function which maps n-bit input to n-bit output. Reversible logic synthesis has been widely studied due to its connection with low-energy computation as well as quantum computation. In this paper, we give a structured decomposition for even RBFs. Specifically, for n ≥ 6, any even n-bit RBF can be decomposed to 7 blocks of (n-1)-bit RBF, where 7 is a constant independent of n and the positions of these blocks have a large degree of freedom. Moreover, if the (n-1)-bit RBFs are required to be even as well, we show for n ≥ 10, even n-bit RBF can be decomposed to 10 even (n - 1)-bit RBFs. In short, our decomposition has block depth 7 and even block depth 10. Our result improves Selinger's work in block depth model, by reducing the constant from 9 to 7 and from 13 to 10, when the blocks are limited to be even. We emphasize that our setting is a bit different from Selinger's work. In Selinger's constructive proof, each block is placed in one of two specific positions and thus the decomposition has an alternating structure. We relax this restriction and allow each block to act on arbitrary (n - 1) bits. This relaxation keeps the block structure and provides more candidates when choosing the positions of blocks.