Haoxuan Xu

Haoxuan Xu, Zeyu He, Mengfan Shen et al.

The task of determining crime types based on criminal behavior facts has become a very important and meaningful task in social science. But the problem facing the field now is that the data samples themselves are unevenly distributed, due to the nature of the crime itself. At the same time, data sets in the judicial field are less publicly available, and it is not practical to produce large data sets for direct training. This article proposes a new training model to solve this problem through NLP processing methods. We first propose a Crime Fact Data Preprocessing Module (CFDPM), which can balance the defects of uneven data set distribution by generating new samples. Then we use a large open source dataset (CAIL-big) as our pretraining dataset and a small dataset collected by ourselves for Fine-tuning, giving it good generalization ability to unfamiliar small datasets. At the same time, we use the improved Bert model with dynamic masking to improve the model. Experiments show that the proposed method achieves state-of-the-art results on the present dataset. At the same time, the effectiveness of module CFDPM is proved by experiments. This article provides a valuable methodology contribution for classifying social science texts such as criminal behaviors. Extensive experiments on public benchmarks show that the proposed method achieves new state-of-the-art results.

Mengyuan Li, Lei Gao, Haoxuan Xu et al.

Every API token you spend is your accumulated wealth; once you can prove its value and the effort behind it, you can resell it. As autonomous agents repeatedly call models and tools, they accumulate memories that are your intellectual property. But today these memories remain private and non-transferable, as there is no way to validate their value. We argue that agent memory can serve as an economic commodity in the agent economy, if buyers can verify that it is authentic, effort-backed, and produced in a compatible execution context. To realize this idea, we propose clawgang, which binds memory to verifiable computational provenance, and meowtrade, a market layer for listing, transferring, and governing certified memory artifacts. Together, they transform one-shot API token spending into reusable and tradable assets, enabling timely memory transfer, reducing repeated exploration, and opening a memory trade market.

Haoxuan Xu, Bo Li, Guanglin Niu

Clothing-change person re-identification (CC Re-ID) has attracted increasing attention in recent years due to its application prospect. Most existing works struggle to adequately extract the ID-related information from the original RGB images. In this paper, we propose an Identity-aware Feature Decoupling (IFD) learning framework to mine identity-related features. Particularly, IFD exploits a dual stream architecture that consists of a main stream and an attention stream. The attention stream takes the clothing-masked images as inputs and derives the identity attention weights for effectively transferring the spatial knowledge to the main stream and highlighting the regions with abundant identity-related information. To eliminate the semantic gap between the inputs of two streams, we propose a clothing bias diminishing module specific to the main stream to regularize the features of clothing-relevant regions. Extensive experimental results demonstrate that our framework outperforms other baseline models on several widely-used CC Re-ID datasets.