Few-shot Named Entity Recognition (NER), the task of identifying named entities with only a limited amount of labeled data, has gained increasing significance in natural language processing. While existing methodologies have shown some effectiveness, such as enriching label semantics through various prompting modes or employing metric learning techniques, their performance exhibits limited robustness across diverse domains due to the lack of rich knowledge in their pre-trained models. To address this issue, we propose CLLMFS, a Contrastive Learning enhanced Large Language Model (LLM) Framework for Few-Shot Named Entity Recognition, achieving promising results with limited training data. Considering the impact of LLM’s internal representations on downstream tasks, CLLMFS integrates Low-Rank Adaptation (LoRA) and contrastive learning mechanisms specifically tailored for few-shot NER. By enhancing the model’s internal representations, CLLMFS effectively improves both entity boundary awareness ability and entity recognition accuracy. Our method has achieved state-of-the-art performance improvements on F1-score ranging from 2.58% to 97.74% over existing best-performing methods across several recognized benchmarks. Furthermore, through cross-domain NER experiments conducted on multiple datasets, we have further validated the robust generalization capability of our method. Our code is available on github (https://github.com/yuzilan/CLLMFS).

Decentralized Finance (DeFi) ecosystem has grown rapidly in the past few years. In the DeFi ecosystem, flash loan is a novel type of uncollateralized loan with nearly negligible lending costs. Malicious attackers can easily borrow a large number of crypto assets, and utilize them to disrupt the price of crypto assets to make a profit. Many flash loan based price manipulation attacks have been reported recently, and caused immense economic losses, e.g., 30 million USD in a single attack. In this paper, we conduct an empirical study on real-world flash loan based attacks in the past two years and present three attack patterns for price manipulation attacks. Then, we propose an approach, LeiShen, to automatically detect price manipulation attacks with asset transfers. We evaluate LeiShen on the first 14,500,000 blocks in Ethereum, and detect 180 attacks with a precision of 78.9%. Among our newly-found attacks, the severest attack has caused a total loss of more than 6.1 million USD.

Abstract The ability of modern detectors to detect small targets is still an unresolved topic compared to their capability of detecting medium and large targets in the field of object detection. Accurately detecting and identifying small objects in the real‐world scenario suffer from sub‐optimal performance due to various factors such as small target size, complex background, variability in illumination, occlusions, and target distortion. Here, a small object detection method for complex traffic scenarios named deformable local and global attention (DLGADet) is proposed, which seamlessly merges the ability of hierarchical attention mechanisms (HAMs) with the versatility of deformable multi‐scale feature fusion, effectively improving recognition and detection performance. First, DLGADet introduces the combination of multi‐scale separable detection and multi‐scale feature fusion mechanism to obtain richer contextual information for feature fusion while solving the misalignment problem of classification and localisation tasks. Second, a deformation feature extraction module (DFEM) is designed to address the deformation of objects. Finally, a HAM combining global and local attention mechanisms is designed to obtain discriminative features from complex backgrounds. Extensive experiments on three datasets demonstrate the effectiveness of the proposed methods. Code is available at https://github.com/ACAMPUS/DLGADet

Product ranking is the core problem for revenue-maximizing online retailers. To design proper product ranking algorithms, various consumer choice models are proposed to characterize the consumers' behaviors when they are provided with a list of products. However, existing works assume that each consumer purchases at most one product or will keep viewing the product list after purchasing a product, which does not agree with the common practice in real scenarios. In this paper, we assume that each consumer can purchase multiple products at will. To model consumers' willingness to view and purchase, we set a random attention span and purchase budget, which determines the maximal amount of products that he/she views and purchases, respectively. Under this setting, we first design an optimal ranking policy when the online retailer can precisely model consumers' behaviors. Based on the policy, we further develop the Multiple-Purchase-with-Budget UCB (MPB-UCB) algorithms with $\tilde{O}(\sqrt{T})$ regret that estimate consumers' behaviors and maximize revenue simultaneously in online settings. Experiments on both synthetic and semi-synthetic datasets prove the effectiveness of the proposed algorithms.

In spite of the tremendous development of recommender system owing to the progressive capability of machine learning recently, the current recommender system is still vulnerable to the distribution shift of users and items in realistic scenarios, leading to the sharp decline of performance in testing environments. It is even more severe in many common applications where only the implicit feedback from sparse data is available. Hence, it is crucial to promote the performance stability of recommendation method in different environments. In this work, we first make a thorough analysis of implicit recommendation problem from the viewpoint of out-of-distribution (OOD) generalization. Then under the guidance of our theoretical analysis, we propose to incorporate the recommendation-specific DAG learner into a novel causal preference-based recommendation framework named CausPref, mainly consisting of causal learning of invariant user preference and anti-preference negative sampling to deal with implicit feedback. Extensive experimental results from real-world datasets clearly demonstrate that our approach surpasses the benchmark models significantly under types of out-of-distribution settings, and show its impressive interpretability.

Point-of-Interest (POI) recommendation is an important task in location-based social networks. It facilitates the relation modeling between users and locations. Recently, researchers recommend POIs by long- and short-term interests and achieve success. However, they fail to well capture the periodic interest. People tend to visit similar places at similar times or in similar areas. Existing models try to acquire such kind of periodicity by user's mobility status or time slot, which limits the performance of periodic interest. To this end, we propose to learn spatial-temporal periodic interest. Specifically, in the long-term module, we learn the temporal periodic interest of daily granularity, then utilize intra-level attention to form long-term interest. In the short-term module, we construct various short-term sequences to acquire the spatial-temporal periodic interest of hourly, areal, and hourly-areal granularities, respectively. Finally, we apply inter-level attention to automatically integrate multiple interests. Experiments on two real-world datasets demonstrate the state-of-the-art performance of our method.

Signed graph representation learning is an effective approach to analyze the complex patterns in real-world signed graphs with the co-existence of positive and negative links. Most previous signed graph representation learning methods resort to balance theory, a classic social theory that originated from psychology as the core assumption. However, since balance theory is shown equivalent to a simple assumption that nodes can be divided into two conflicting groups, it fails to model the structure of real signed graphs. To solve this problem, we propose Group Signed Graph Neural Network (GS-GNN) model for signed graph representation learning beyond the balance theory assumption. GS-GNN has a dual GNN architecture that consists of the global and the local module. In the global module, we adopt a more generalized assumption that nodes can be divided into multiple latent groups and that the groups can have arbitrary relations and propose a novel prototype-based GNN to learn node representations based on the assumption. In the local module, to give the model enough flexibility in modeling other factors, we do not make any prior assumptions, treat positive links and negative links as two independent relations, and adopt a relational GNN to learn node representations. Both modules can complement each other, and the concatenation of two modules is fed into downstream tasks. Extensive experimental results demonstrate the effectiveness of our GS-GNN model on both synthetic and real-world signed graphs by greatly and consistently outperforming all the baselines and achieving new state-of-the-art results. Our implementation is available in PyTorch.