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Xiang Ao

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17 papers

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EAAI Journal 2026 Journal Article

Enhancing small object detection in low-altitude remote sensing via high-resolution feature extraction and multi-scale fusion

Xinyuan Le
Ying Chen
Wei Zeng
Xiang Ao
Huiling Chen
Jingyan Xie

To address insufficient feature representation, redundancy in multi-scale fusion, and weak directional perception in low-altitude remote sensing images, this paper proposes a detection model based on convolutional neural networks and attention mechanisms, named Multi-Scale Fusion You Only Look Once (MSF-YOLO), with enhanced feature extraction and multi-scale fusion. First, the detection head hierarchy is reconstructed by adding a P2 tiny object head and removing the large object head, enhancing high-resolution feature utilization. Second, the Improved Selective Contour Aggregation (ISBA) module is designed to construct the Improved Selective Contour Aggregation Network (ISBANet), which dynamically adjusts fusion weights and performs consistency correction. Third, Enhanced Spatial and Directional Convolution (ESDConv) is proposed, improving small-object feature representation via spatial slicing-channel concatenation and multi-direction convolution while avoiding detail loss from traditional downsampling. Experimental results on the Vision Meets Drone Object Detection in Image Challenge (2019) (VisDrone-DET2019) dataset show that MSF-YOLO-n achieves detection accuracy close to You Only Look Once version 8x (YOLOv8x) with only 4% of its parameters and 18. 4% of its computational cost. Compared to baseline You Only Look Once version 8n (YOLOv8n), MSF-YOLO-n reduces parameters by 10% while increasing mean average precision at IoU thresholds 50–95% (mAP50-95) by 9. 3%, albeit with an expected increase in floating-point operations (FLOPs). Verification on the Unmanned Aerial Vehicle Benchmark Object Detection and Tracking (UAVDT) dataset confirms the model's generalization ability, demonstrating the effectiveness of the proposed artificial intelligence method for low-altitude remote sensing small-object detection.

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AAAI Conference 2025 Conference Paper

AlphaForge: A Framework to Mine and Dynamically Combine Formulaic Alpha Factors

Hao Shi
Weili Song
Xinting Zhang
Jiahe Shi
Cuicui Luo
Xiang Ao
Hamid Arian
Luis Angel Seco

The complexity of financial data, characterized by its variability and low signal-to-noise ratio, necessitates advanced methods in quantitative investment that prioritize both performance and interpretability.Transitioning from early manual extraction to genetic programming, the most advanced approach in the alpha factor mining domain currently employs reinforcement learning to mine a set of combination factors with fixed weights. However, the performance of resultant alpha factors exhibits inconsistency, and the inflexibility of fixed factor weights proves insufficient in adapting to the dynamic nature of financial markets. To address this issue, this paper proposes a two-stage formulaic alpha generating framework AlphaForge, for alpha factor mining and factor combination. This framework employs a generative-predictive neural network to generate factors, leveraging the robust spatial exploration capabilities inherent in deep learning while concurrently preserving diversity. The combination model within the framework incorporates the temporal performance of factors for selection and dynamically adjusts the weights assigned to each component alpha factor. Experiments conducted on real-world datasets demonstrate that our proposed model outperforms contemporary benchmarks in formulaic alpha factor mining. Furthermore, our model exhibits a notable enhancement in portfolio returns within the realm of quantitative investment and real money investment.

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AAAI Conference 2025 Conference Paper

Controlling Large Language Models Through Concept Activation Vectors

Hanyu Zhang
Xiting Wang
Chengao Li
Xiang Ao
Qing He

As large language models (LLMs) are widely deployed across various domains, the ability to control their generated outputs has become more critical. This control involves aligning LLMs outputs with human values and ethical principles or customizing LLMs on specific topics or styles for individual users. Existing controlled generation methods either require significant computational resources and extensive trial-and-error or provide coarse-grained control. In this paper, we propose Generation with Concept Activation Vector (GCAV), a lightweight model control framework that ensures accurate control without requiring resource-extensive fine-tuning. Specifically, GCAV first trains a concept activation vector for specified concepts to be controlled, such as toxicity. During inference, GCAV steers the concept vector in LLMs, for example, by removing the toxicity concept vector from the activation layers. Control experiments from different perspectives, including toxicity reduction, sentiment control, linguistic style, and topic control, demonstrate that our framework achieves state-of-the-art performance with granular control, allowing for fine-grained adjustments of both the steering layers and the steering magnitudes for individual samples.

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AAAI Conference 2025 Conference Paper

Dynamic Graph Learning with Static Relations for Credit Risk Assessment

Qi Yuan
Yang Liu
Yateng Tang
Xinhuan Chen
Xuehao Zheng
Qing He
Xiang Ao

Credit risk assessment has increasingly become a prominent research field due to the dramatically increased incidents of financial default. Traditional graph-based methods have been developed to detect defaulters within user-merchant commercial payment networks. However, these methods face challenges in detecting complex risks, primarily due to their neglect of user-to-user fund transfer interactions and the under-utilization of temporal information. In this paper, we propose a novel framework named Dynamic Graph Neural Network with Static Relations (DGNN-SR) for credit risk assessment, which can encode the dynamic transaction graph and the static fund transfer graph simultaneously. To fully harness the temporal information, DGNN-SR employs a multi-view time encoder to explore the semantics of both relative and absolute time. To enhance the dynamic representations with static relations, we devise an adaptive re-weighting strategy to incorporate the static relations into the dynamic representations of time encoder, which extracts more discriminative features for risk assessment. Extensive experiments on two real-world business datasets demonstrate that our proposed method achieves a 0.85% - 2.5% improvement over existing SOTA methods.

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AAAI Conference 2025 Conference Paper

Online Fraud Detection via Test-Time Retrieval-Based Representation Enrichment

Yiran Qiao
Ningtao Wang
Yuncong Gao
Yang Yang
Xing Fu
Weiqiang Wang
Xiang Ao

Anti-fraud machine learning systems are perpetually confronted with the significant challenge of concept drift, driven by the continuous and intense evolution of fraudulent techniques. That is, outdated models trained on historical fraudulent behaviors often fall short in addressing the evolving tactics of malicious users over time. The key issue lies in effectively tackling the rapid and significant evolution of fraudsters' behaviors to detect these emerging and unforeseen anomalies. In this paper, we propose a solution by directly accessing real-time data and introducing a lightweight plug-in approach named TRE (Test-time Retrieval-based Representation Enrichment). Considering the similarity among samples, TRE employs a retriever to efficiently identify the top-K most relevant recent samples and implements an aggregation strategy to provide neighboring embeddings to the predictor. It thus adjusts the trained classifiers during the test time, providing them with the information from the latest unlabeled data. Extensive experiments on three large-scale real-world datasets demonstrate the superiority of TRE. By consistently incorporating information from the nearest neighbors, TRE demonstrates high adaptability and surpasses existing methods in performance.

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AAAI Conference 2024 Conference Paper

Online Conversion Rate Prediction via Multi-Interval Screening and Synthesizing under Delayed Feedback

Qiming Liu
Xiang Ao
Yuyao Guo
Qing He

Due to the widespread adoption of the cost-per-action(CPA) display strategy that demands a real-time conversion rate prediction(CVR), delayed feedback is becoming one of the major challenges in online advertising. As the true labels of a significant quantity of samples are only available after long delays, the observed training data are usually biased, harming the performance of models. Recent studies show integrating models with varying waiting windows to observe true labels is beneficial, but the aggregation framework remains far from reaching a consensus. In this work, we propose the Multi-Interval Screening and Synthesizing model (MISS for short) for online CVR prediction. We first design a multi-interval screening model with various output heads to produce accurate and distinctive estimates. Then a light-weight synthesizing model with an assembled training pipeline is applied to thoroughly exploit the knowledge and relationship among heads, obtaining reliable predictions. Extensive experiments on two real-world advertising datasets validate the effectiveness of our model.

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AAAI Conference 2023 Conference Paper

Defending against Backdoor Attacks in Natural Language Generation

Xiaofei Sun
Xiaoya Li
Yuxian Meng
Xiang Ao
Lingjuan Lyu
Jiwei Li
Tianwei Zhang

The frustratingly fragile nature of neural network models make current natural language generation (NLG) systems prone to backdoor attacks and generate malicious sequences that could be sexist or offensive. Unfortunately, little effort has been invested to how backdoor attacks can affect current NLG models and how to defend against these attacks. In this work, by giving a formal definition of backdoor attack and defense, we investigate this problem on two important NLG tasks, machine translation and dialog generation. Tailored to the inherent nature of NLG models (e.g., producing a sequence of coherent words given contexts), we design defending strategies against attacks. We find that testing the backward probability of generating sources given targets yields effective defense performance against all different types of attacks, and is able to handle the one-to-many issue in many NLG tasks such as dialog generation. We hope that this work can raise the awareness of backdoor risks concealed in deep NLG systems and inspire more future work (both attack and defense) towards this direction.

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AAAI Conference 2023 Conference Paper

Gradient-Adaptive Pareto Optimization for Constrained Reinforcement Learning

Zixian Zhou
Mengda Huang
Feiyang Pan
Jia He
Xiang Ao
Dandan Tu
Qing He

Constrained Reinforcement Learning (CRL) burgeons broad interest in recent years, which pursues maximizing long-term returns while constraining costs. Although CRL can be cast as a multi-objective optimization problem, it is still facing the key challenge that gradient-based Pareto optimization methods tend to stick to known Pareto-optimal solutions even when they yield poor returns (e.g., the safest self-driving car that never moves) or violate the constraints (e.g., the record-breaking racer that crashes the car). In this paper, we propose Gradient-adaptive Constrained Policy Optimization (GCPO for short), a novel Pareto optimization method for CRL with two adaptive gradient recalibration techniques. First, to find Pareto-optimal solutions with balanced performance over all targets, we propose gradient rebalancing which forces the agent to improve more on under-optimized objectives at every policy iteration. Second, to guarantee that the cost constraints are satisfied, we propose gradient perturbation that can temporarily sacrifice the returns for costs. Experiments on the SafetyGym benchmarks show that our method consistently outperforms previous CRL methods in reward while satisfying the constraints.

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AAAI Conference 2022 Conference Paper

Mind the Gap: Cross-Lingual Information Retrieval with Hierarchical Knowledge Enhancement

Fuwei Zhang
Zhao Zhang
Xiang Ao
Dehong Gao
Fuzhen Zhuang
Yi Wei
Qing He

Cross-Lingual Information Retrieval (CLIR) aims to rank the documents written in a language different from the user’s query. The intrinsic gap between different languages is an essential challenge for CLIR. In this paper, we introduce the multilingual knowledge graph (KG) to the CLIR task due to the sufficient information of entities in multiple languages. It is regarded as a “silver bullet” to simultaneously perform explicit alignment between queries and documents and also broaden the representations of queries. And we propose a model named CLIR with hierarchical knowledge enhancement (HIKE) for our task. The proposed model encodes the textual information in queries, documents and the KG with multilingual BERT, and incorporates the KG information in the query-document matching process with a hierarchical information fusion mechanism. Particularly, HIKE first integrates the entities and their neighborhood in KG into query representations with a knowledge-level fusion, then combines the knowledge from both source and target languages to further mitigate the linguistic gap with a language-level fusion. Finally, experimental results demonstrate that HIKE achieves substantial improvements over state-ofthe-art competitors.

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YNIMG Journal 2022 Journal Article

Modulating social feedback processing by deep TMS targeting the medial prefrontal cortex: Behavioral and electrophysiological manifestations

Dandan Zhang
Xiang Ao
Zixin Zheng
Junshi Shen
Yueyao Zhang
Ruolei Gu

Neuroimaging studies have suggested that the medial prefrontal cortex (mPFC) is a key brain region for social feedback processing, but previous findings are largely based on correlational approaches. In this study, we use the deep transcranial magnetic stimulation (dTMS) to manipulate mPFC activity, then investigate participants' behavioral performance and event-related potentials (ERPs) during the Social Judgment Paradigm. A between-subject design was applied, such that both the active dTMS group and the sham group consisted of 30 participants. We found that the sham group was more likely to predict that they would be socially accepted (rather than rejected) by peers, but the same was not true in the active group. Additionally, this study is the first one to observe ERP signal changes in response to dTMS manipulation. ERP results show that both the expectation stage and the experience stage of social feedback processing were modulated by dTMS: (1) at the expectation stage, the P1 component was smaller in the active group than the sham group, while the stimulus-preceding negativity showed a stronger differentiating effect between positive and negative prediction in the sham group than the active group; (2) at the experience stage, the sensitivity of the late positive potential to the valence and predictability of social feedback was stronger in the sham group than the active group. These results improve our understanding about the relationship between the mPFC and social feedback processing.

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AAAI Conference 2021 Conference Paper

A User-Adaptive Layer Selection Framework for Very Deep Sequential Recommender Models

Lei Chen
Fajie Yuan
Jiaxi Yang
Xiang Ao
Chengming Li
Min Yang

Sequential recommender systems (SRS) have become a research hotspot in recent studies. Because of the requirement in capturing user’s dynamic interests, sequential neural network based recommender models often need to be stacked with more hidden layers (e. g. , up to 100 layers) compared with standard collaborative filtering methods. However, the high network latency has become the main obstacle when deploying very deep recommender models into a production environment. In this paper, we argue that the typical prediction framework that treats all users equally during the inference phase is inefficient in running time, as well as sub-optimal in accuracy. To resolve such an issue, we present SkipRec, an adaptive inference framework by learning to skip inactive hidden layers on a per-user basis. Specifically, we devise a policy network to automatically determine which layers should be retained and which layers are allowed to be skipped, so as to achieve user-specific decisions. To derive the optimal skipping policy, we propose using gumbel softmax and reinforcement learning to solve the non-differentiable problem during backpropagation. We perform extensive experiments on three real-world recommendation datasets, and demonstrate that SkipRec attains comparable or better accuracy with much less inference time.

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AAAI Conference 2021 Conference Paper

Imagine, Reason and Write: Visual Storytelling with Graph Knowledge and Relational Reasoning

Chunpu Xu
Min Yang
Chengming Li
Ying Shen
Xiang Ao
Ruifeng Xu

Visual storytelling is the task of generating a short story to describe an ordered image stream. Different from visual captions, stories contain not only factual descriptions, but also imaginary concepts that do not appear in the images. In this paper, we propose a novel imagine-reason-write generation framework (IRW) for visual storytelling, inspired by the logic of humans when they write a story. First, a multimodal imagining module is leveraged to learn the imaginative storyline explicitly, improving the coherence and reasonability of the generated story. Second, we employ a relational reasoning module to fully exploit the external knowledge (commonsense knowledge base) and task-specific knowledge (scene graph and event graph) with a relational reasoning method based on the storyline. In this way, we can effectively capture the most informative commonsense and visual relationships among objects in images, enhancing the diversity and informativeness of the generated story. Finally, we integrate the visual information and semantic (concept) information to generate human-like stories. Extensive experiments on a benchmark dataset (i. e. , VIST) demonstrate that the proposed IRW framework substantially outperforms the state-of-the-art methods across multiple evaluation metrics.

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IJCAI Conference 2021 Conference Paper

Online Credit Payment Fraud Detection via Structure-Aware Hierarchical Recurrent Neural Network

Wangli Lin
Li Sun
Qiwei Zhong
Can Liu
Jinghua Feng
Xiang Ao
Hao Yang

Online credit payment fraud detection plays a critical role in financial institutions due to the growing volume of fraudulent transactions. Recently, researchers have shown an increased interest in capturing users’ dynamic and evolving fraudulent tendencies from their behavior sequences. However, most existing methodologies for sequential modeling overlook the intrinsic structure information of web pages. In this paper, we adopt multi-scale behavior sequence generated from different granularities of web page structures and propose a model named SAH-RNN to consume the multi-scale behavior sequence for online payment fraud detection. The SAH-RNN has stacked RNN layers in which upper layers modeling for compendious behaviors are updated less frequently and receive the summarized representations from lower layers. A dual attention is devised to capture the impacts on both sequential information within the same sequence and structural information among different granularity of web pages. Experimental results on a large-scale real-world transaction dataset from Alibaba show that our proposed model outperforms state-of-the-art models. The code is available at https: //github. com/WangliLin/SAH-RNN.

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IJCAI Conference 2020 Conference Paper

Towards Explainable Conversational Recommendation

Zhongxia Chen
Xiting Wang
Xing Xie
Mehul Parsana
Akshay Soni
Xiang Ao
Enhong Chen

Recent studies have shown that both accuracy and explainability are important for recommendation. In this paper, we introduce explainable conversational recommendation, which enables incremental improvement of both recommendation accuracy and explanation quality through multi-turn user-model conversation. We show how the problem can be formulated, and design an incremental multi-task learning framework that enables tight collaboration between recommendation prediction, explanation generation, and user feedback integration. We also propose a multi-view feedback integration method to enable effective incremental model update. Empirical results demonstrate that our model not only consistently improves the recommendation accuracy but also generates explanations that fit user interests reflected in the feedbacks.

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TIST Journal 2019 Journal Article

Large-Scale Frequent Episode Mining from Complex Event Sequences with Hierarchies

Xiang Ao
Haoran Shi
Jin Wang
Luo Zuo
Hongwei Li
Qing He

Frequent Episode Mining (FEM), which aims at mining frequent sub-sequences from a single long event sequence, is one of the essential building blocks for the sequence mining research field. Existing studies about FEM suffer from unsatisfied scalability when faced with complex sequences as it is an NP-complete problem for testing whether an episode occurs in a sequence. In this article, we propose a scalable, distributed framework to support FEM on “big” event sequences. As a rule of thumb, “big” illustrates an event sequence is either very long or with masses of simultaneous events. Meanwhile, the events in this article are arranged in a predefined hierarchy. It derives some abstractive events that can form episodes that may not directly appear in the input sequence. Specifically, we devise an event-centered and hierarchy-aware partitioning strategy to allocate events from different levels of the hierarchy into local processes. We then present an efficient special-purpose algorithm to improve the local mining performance. We also extend our framework to support maximal and closed episode mining in the context of event hierarchy, and to the best of our knowledge, we are the first attempt to define and discover hierarchy-aware maximal and closed episodes. We implement the proposed framework on Apache Spark and conduct experiments on both synthetic and real-world datasets. Experimental results demonstrate the efficiency and scalability of the proposed approach and show that we can find practical patterns when taking event hierarchies into account.

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IJCAI Conference 2019 Conference Paper

Unsupervised Neural Aspect Extraction with Sememes

Ling Luo
Xiang Ao
Yan Song
Jinyao Li
Xiaopeng Yang
Qing He
Dong Yu

Aspect extraction relies on identifying aspects by discovering coherence among words, which is challenging when word meanings are diversified and processing on short texts. To enhance the performance on aspect extraction, leveraging lexical semantic resources is a possible solution to such challenge. In this paper, we present an unsupervised neural framework that leverages sememes to enhance lexical semantics. The overall framework is analogous to an autoenoder which reconstructs sentence representations and learns aspects by latent variables. Two models that form sentence representations are proposed by exploiting sememes via (1) a hierarchical attention; (2) a context-enhanced attention. Experiments on two real-world datasets demonstrate the validity and the effectiveness of our models, which significantly outperforms existing baselines.

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IJCAI Conference 2018 Conference Paper

Beyond Polarity: Interpretable Financial Sentiment Analysis with Hierarchical Query-driven Attention

Ling Luo
Xiang Ao
Feiyang Pan
Jin Wang
Tong Zhao
Ningzi Yu
Qing He

Sentiment analysis has played a significant role in financial applications in recent years. The informational and emotive aspects of news texts may affect the prices, volatilities, volume of trades, and even potential risks of financial subjects. Previous studies in this field mainly focused on identifying polarity~(e. g. positive or negative). However, as financial decisions broadly require justifications, only plausible polarity cannot provide enough evidence during the decision making processes of humanity. Hence an explainable solution is in urgent demand. In this paper, we present an interpretable neural net framework for financial sentiment analysis. First, we design a hierarchical model to learn the representation of a document from multiple granularities. In addition, we propose a query-driven attention mechanism to satisfy the unique characteristics of financial documents. With the domain specified questions provided by the financial analysts, we can discover different spotlights for queries from different aspects. We conduct extensive experiments on a real-world dataset. The results demonstrate that our framework can learn better representation of the document and unearth meaningful clues on replying different users? preferences. It also outperforms the state-of-the-art methods on sentiment prediction of financial documents.

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