With the rapid development of modern communication technology, telecom fraud has been increasing year by year. If fraudsters can be accurately identified before they carry out their scams, it can not only protect people from potential losses but also increase trust in telecom operators. Therefore, in recent years, telecom fraud detection has garnered widespread attention in both academia and industry. Although existing methods for telecom fraud detection have achieved good performance, there are still many unresolved issues for real-world telecom operators. First, existing methods only focus on a single telecom scenario, while real-world telecom scenarios are diverse. Utilizing the characteristics of these different telecom scenarios can improve the effectiveness of telecom fraud detection. Second, existing methods usually use Graph Neural Networks (GNNs) to aggregate neighbor information. However, real-world telecom operators can’t obtain information of users from other operators, resulting in the lacking destination node attributes, which degenerates the performance of GNNs. To address the above issues, in this paper, we propose a new model for Telecom Fraud Detection Based on Feature binning and Autoencoder (TFD-FA). In TFD-FA, a feature binning framework is designed to partition users into different telecom scenarios in order to reflect their unique characteristics. An autoencoder component is also designed to aggregate neighbor information. Furthermore, an imbalance classifier component is constructed to solve the problem of the significantly lower number of fraudsters compared to normal users. Extensive experiments in a real-world dataset demonstrate the effectiveness of TFD-FA, which outperforms the compared baseline models.

Anchor-free temporal action detection methods have recently achieved many good results in solving the problem of flexible boundaries and different duration of actions. But the anchor-free methods use local features to predict the action boundaries so that it is sensitive to noises and prone to generate incomplete action proposals. Moreover, there exist long-term temporal dependencies between actions and temporal semantic consistency between action primitives in the same classes of actions. Therefore, we propose a snippet-level supervised contrastive learning-based transformer (SSCL-T) model for temporal action detection, which can learn semantically local and global temporal relationships in actions. This model learns the local temporal dynamic features of actions through local temporal coding and uses the transformer to model the global semantic dependencies between long-term actions. In addition, we utilize the action class information to learn the high-level semantic features of actions by designing a snippet-level supervised contrastive learning, forcing the temporal dynamic features of the same class of actions to be as close as possible and the features of different classes of actions to be as far away as possible, thus effectively realizing accurate prediction of action boundaries. Our model has been verified on two benchmark datasets ActivityNet-v1.3 and THUMOS14. The experimental results demonstrate that the proposed model has significantly improved on both datasets. Compared with the benchmark method BMN, the average mAP value has increased by 2.91% and 8.4% on ActivityNet-v1.3 and THUMOS14, respectively.

Representation and reasoning about strategic abilities has been an active research area in AI and multi-agent systems. Many variations and extensions of alternating-time temporal logic ATL have been proposed. However, most of the logical frameworks ignore the issue of coordination within a coalition, and are unable to specify the internal structure of strategies. In this paper, we propose JAADL, a modal logic for joint abilities under strategy commitments, which is an extension of ATL. Firstly, we introduce an operator of elimination of (strictly) dominated strategies, with which we can represent joint abilities of coalitions. Secondly, our logic is based on linear dynamic logic (LDL), an extension of linear temporal logic (LTL), so that we can use regular expressions to represent commitments to structured strategies. We analyze valid formulas in JAADL, give sufficient/necessary conditions for joint abilities, and show that model checking memoryless JAADL is in EXPTIME.