Address is a structured description used to identify a specific place or point of interest, and it provides an effective way to locate people or objects. The standardization of Chinese place name and address occupies an important position in the construction of a smart city. Traditional address specification technology often adopts methods based on text similarity or rule bases, which cannot handle complex, missing, and redundant address information well. This paper transforms the task of address standardization into calculating the similarity of address pairs, and proposes a contrast learning address matching model based on the attention-Bi-LSTM-CNN network (ABLC). First of all, ABLC use the Trie syntax tree algorithm to extract Chinese address elements. Next, based on the basic idea of contrast learning, a hybrid neural network is applied to learn the semantic information in the address. Finally, Manhattan distance is calculated as the similarity of the two addresses. Experiments on the self-constructed dataset with data augmentation demonstrate that the proposed model has better stability and performance compared with other baselines.

In representation learning domain, the mainstream methods for model ensemble include “implicit” ensemble approaches, such as using techniques like dropout, and “explicit” ensemble methods, such as voting or weighted averaging based on multiple model outputs. Compared to implicit ensemble techniques, explicit ensemble methods allow for more flexibility in combining models with different structures to obtain different perspectives on representations. However, the representations obtained from different models do not guarantee a linear relationship, and simply linearly combining multiple model outputs may result in a degraded performance. Meanwhile, employing non‐linear fusion mechanisms such as distillation and meta‐learning can be uninterpretable and time‐consuming. To this end, we propose the hypothesis of linear fusion based on the output representations of deep learning models, and design a interpretable linear fusion method based on this hypothesis. This method applies a transform layer to map the output representations of different models to the same classification center. Experimental results demonstrate that compared to directly averaging the representations, our method achieves better performance. Additionally, our method retains the convenience of direct averaging while offering improved performance in terms of time and computational efficiency compared to non‐linear fusion. Furthermore, we test the applicability of our method in both computer vision and natural language processing representation tasks using supervised and semi‐supervised approaches.

Government hotline plays a significant role in meeting the demands of the people and resolving social conflicts in China. In this paper, we propose an automatic work‐order assignment method based on event extraction and external knowledge to address the problem of low efficiency with manual assignment for Chinese government hotline. Our proposed assignment method is composed of four parts: (1) Semantic encoding layer, which extracts semantic information from the work‐order text and obtains semantic representation vectors with contextual feature information. (2) Event extraction layer which extracts the local features and global features from the semantic representation vectors with the help of the CRF network to enhance event extraction effect. (3) External knowledge embedding layer, which integrates ‘rights and responsibilities lists’ with the historical information of the work‐order to assist assignment. (4) Assignment layer which completes work‐order assignment by combining two output vectors from event extraction layer and external knowledge embedding layer. Experimental results show our proposed method can achieve better assignment performance compared with several baseline methods.

This paper presents a comprehensive study on the use of ensemble Reinforcement Learning (RL) models in financial trading strategies, leveraging classifier models to enhance performance. By combining RL algorithms such as A2C, PPO, and SAC with traditional classifiers like Support Vector Machines (SVM), Decision Trees, and Logistic Regression, we investigate how different classifier groups can be integrated to improve risk-return trade-offs. The study evaluates the effectiveness of various ensemble methods, comparing them with individual RL models across key financial metrics, including Cumulative Returns, Sharpe Ratios (SR), Calmar Ratios, and Maximum Drawdown (MDD). Our results demonstrate that ensemble methods consistently outperform base models in terms of risk-adjusted returns, providing better management of drawdowns and overall stability. However, we identify the sensitivity of ensemble performance to the choice of variance threshold τ , highlighting the importance of dynamic τ adjustment to achieve optimal performance. This study emphasizes the value of combining RL with classifiers for adaptive decision-making, with implications for financial trading, robotics, and other dynamic environments.

OD matrix estimation is a critical problem in the transportation domain. The principle method uses the traffic sensor measured information such as traffic counts to estimate the traffic demand represented by the OD matrix. The problem is divided into two categories: static OD matrix estimation and dynamic OD matrices sequence(OD sequence for short) estimation. The above two face the underdetermination problem caused by abundant estimated parameters and insufficient constraint information. In addition, OD sequence estimation also faces the lag challenge: due to different traffic conditions such as congestion, identical vehicle will appear on different road sections during the same observation period, resulting in identical OD demands correspond to different trips. To this end, this paper proposes an integrated method, which uses deep learning methods to infer the structure of OD sequence and uses structural constraints to guide traditional numerical optimization. Our experiments show that the neural network(NN) can effectively infer the structure of the OD sequence and provide practical constraints for numerical optimization to obtain better results. Moreover, the experiments show that provided structural information contains not only constraints on the spatial structure of OD matrices but also provides constraints on the temporal structure of OD sequence, which solve the effect of the lagging problem well.

The estimation of origin-destination (OD) matrices is a crucial aspect of Intelligent Transport Systems (ITS). It involves adjusting an initial OD matrix by regressing the current observations like traffic counts of road sections (e.g., using least squares). However, the OD estimation problem lacks sufficient constraints and is mathematically underdetermined. To alleviate this problem, some researchers incorporate a prior OD matrix as a target in the regression to provide more structural constraints. However, this approach is highly dependent on the existing prior matrix, which may be outdated. Others add structural constraints through sensor data, such as vehicle trajectory and speed, which can reflect more current structural constraints in real-time. Our proposed method integrates deep learning and numerical optimization algorithms to infer matrix structure and guide numerical optimization. This approach combines the advantages of both deep learning and numerical optimization algorithms. The neural network(NN) learns to infer structural constraints from probe traffic flows, eliminating dependence on prior information and providing real-time performance. Additionally, due to the generalization capability of NN, this method is economical in engineering. We conducted tests to demonstrate the good generalization performance of our method on a large-scale synthetic dataset. Subsequently, we verified the stability of our method on real traffic data. Our experiments provided confirmation of the benefits of combining NN and numerical optimization.