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Jiahao Sun

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7 papers

2 author rows

AAAI Conference 2026 Conference Paper

Privacy on the Fly: A Predictive Adversarial Transformation Network for Mobile Sensor Data

Tianle Song
Chenhao Lin
Yang Cao
Zhengyu Zhao
Jiahao Sun
Chong Zhang
Le Yang
Chao Shen

Mobile motion sensors such as accelerometers and gyroscopes are now ubiquitously accessible by third-party apps via standard APIs. While enabling rich functionalities like activity recognition and step counting, this openness has also enabled unregulated inference of sensitive user traits, such as gender, age, and even identity, without user consent. Existing privacy-preserving techniques, such as GAN-based obfuscation or differential privacy, typically require access to the full input sequence, introducing latency that is incompatible with real-time scenarios. Worse, they tend to distort temporal and semantic patterns, degrading the utility of the data for benign tasks like activity recognition. To address these limitations, we propose the Predictive Adversarial Transformation Network (PATN), a real-time privacy-preserving framework that leverages historical signals to generate adversarial perturbations proactively. The perturbations are applied immediately upon data acquisition, enabling continuous protection without disrupting application functionality. Experiments on two datasets demonstrate that PATN substantially degrades the performance of privacy inference models, achieving Attack Success Rate (ASR) of 40.11% and 44.65% (reducing inference accuracy to near-random) and increasing the Equal Error Rate (EER) from 8.30% and 7.56% to 41.65% and 46.22%. On ASR, PATN outperforms baseline methods by 16.16% and 31.96%, respectively.

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JBHI Journal 2026 Journal Article

Privacy Preserved Blood Glucose Level Cross-Prediction: An Asynchronous Decentralized Federated Learning Approach

Chengzhe Piao
Taiyu Zhu
Yu Wang
Stephanie E Baldeweg
Paul Taylor
Pantelis Georgiou
Jiahao Sun
Jun Wang

Newly diagnosed Type 1 Diabetes (T1D) patients often struggle to obtain effective Blood Glucose (BG) prediction models due to the lack of sufficient BG data from Continuous Glucose Monitoring (CGM), presenting a significant “cold start” problem in patient care. Utilizing population models to address this challenge is a potential solution, but collecting patient data for training population models in a privacy-conscious manner is challenging, especially given that such data is often stored on personal devices. Considering the privacy protection and addressing the “cold start” problem in diabetes care, we propose “GluADFL”, blood Glucose prediction by Asynchronous Decentralized Federated Learning. We compared GluADFL with eight baseline methods using four distinct T1D datasets, comprising 298 participants, which demonstrated its superior performance in accurately predicting BG levels for cross-patient analysis. Furthermore, patients’ data might be stored and shared across various communication networks in GluADFL, ranging from highly interconnected (e. g. , random, performs the best among others) to more structured topologies (e. g. , cluster and ring), suitable for various social networks. The asynchronous training framework supports flexible participation. By adjusting the ratios of inactive participants, we found it remains stable if less than 70% are inactive. Our results confirm that GluADFL offers a practical, privacy-preserved solution for BG prediction in T1D, significantly enhancing the quality of diabetes management.

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EAAI Journal 2025 Journal Article

A swarm intelligence framework in complex environments: Optimizing area coverage guidance and control

Jiahao Sun
Sen Han
Shifeng Ding
Lingxiao Yan
Fang Li
Li Zhou

With the development of artificial intelligence technology, deploying multiple Unmanned Surface Vehicles (multi-USVs) enhances efficiency and safety but introduces challenges including environmental disturbances, regulatory compliance (COLREGs), and collision avoidance. This study proposes an integrated framework addressing these through the Theta-Integrated Divide Areas Trajectory Planning (TDAP) algorithm for dynamic-constrained coverage planning, Nonlinear Model Predictive Control (NMPC) for robust trajectory tracking under wind/current variations, and a Dynamic Theta* (D-Theta*) algorithm with virtual obstacle-lines for COLREGs-compliant collision avoidance—including emergency evasion when target ships fail to act. Simulations demonstrate high coverage efficiency, precise trajectory tracking, and consistently safe navigation across diverse scenarios. The framework significantly improves multi-USV coordination and safety in complex environments, enabling reliable autonomous operations without direct human intervention.

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AIIM Journal 2025 Journal Article

DMHGNN: Double multi-view heterogeneous graph neural network framework for drug-target interaction prediction

Qiao Ning
Yue Wang
Yaomiao Zhao
Jiahao Sun
Lu Jiang
Kaidi Wang
Minghao Yin

Accurate identification of drug-target interactions (DTIs) plays a crucial role in drug discovery. Compared with traditional experimental methods that are labor-intensive and time-consuming, computational methods for drug-target interactions prediction are more popular in recent years. Conventional computational methods almost simply view heterogeneous network constructed by the drug-related and protein-related dataset instead of comprehensively exploring drug-protein pair (DPP) information. To address this limitation, we proposed a Double Multi-view Heterogeneous Graph Neural Network framework for drug-target interaction prediction (DMHGNN). In DMHGNN, one multi-view heterogeneous graph neural network is based on meta-paths and denoising autoencoder for protein-, drug-related heterogeneous network learning, and another multi-view heterogeneous graph neural network is based on multi-channel graph convolutional network for drug-protein pair similarity network learning. First, a meta-path-based graph encoder with the attention mechanism is used for substructure learning of complex relationships from heterogeneous network constructed by proteins, drugs, side-effects and diseases, obtaining key information that is easy to be ignored in global learning of heterogeneous networks, and multi-source neighbouring features for drugs and proteins are learned from heterogeneous network via denoising auto-encoder model. Then, multi-view graphs of drug-protein pairs (DPPs) including the topology graph, semantics graph and collaborative graph with shared weights are constructed, and the multi-channel graph convolutional network (GCN) is utilized to learn the deep representation of DPPs. Finally, a multi-layer fully connection network is trained to predict drug-target interactions. Experiments have demonstrated its effectiveness and better performance than state-of-the-art methods.

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ICRA Conference 2024 Conference Paper

Autonomous Implicit Indoor Scene Reconstruction with Frontier Exploration

Jing Zeng
Yanxu Li
Jiahao Sun
Qi Ye 0001
Yunlong Ran
Jiming Chen 0001

Implicit neural representations have demonstrated significant promise for 3D scene reconstruction. Recent works have extended their applications to autonomous implicit reconstruction through the Next Best View (NBV) based method. However, the NBV method cannot guarantee complete scene coverage and often necessitates extensive viewpoint sampling, particularly in complex scenes. In the paper, we propose to 1) incorporate frontier-based exploration tasks for global coverage with implicit surface uncertainty-based reconstruction tasks to achieve high-quality reconstruction. and 2) introduce a method to achieve implicit surface uncertainty using color uncertainty, which reduces the time needed for view selection. Further with these two tasks, we propose an adaptive strategy for switching modes in view path planning, to reduce time and maintain superior reconstruction quality. Our method exhibits the highest reconstruction quality among all planning methods and superior planning efficiency in methods involving reconstruction tasks. We deploy our method on a UAV and the results show that our method can plan multi-task views and reconstruct a scene with high quality.

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AAMAS Conference 2024 Conference Paper

Mutual Information as Intrinsic Reward of Reinforcement Learning Agents for On-demand Ride Pooling

Xianjie Zhang
Jiahao Sun
Chen Gong
Kai Wang
Yifei Cao
Hao Chen
Yu Liu

The emergence of on-demand ride pooling services allows each vehicle to serve multiple passengers at a time, thus increasing drivers’ income and enabling passengers to travel at lower prices than taxi/car on-demand services. Although on-demand ride pooling services can bring so many benefits, ride pooling services need a well-defined matching strategy to maximize the benefits for all parties (passengers, drivers, aggregation companies and environment), especially the regional dispatching of vehicles has a significant impact on matching and revenue. Existing algorithms often only consider revenue maximization, which makes it difficult for requests with unusual distribution to get rides. How to increase revenue while ensuring a reasonable assignment of requests brings a challenge to ride pooling service companies (aggregation companies). In this paper, we propose a framework for vehicle dispatching for ride pooling tasks, which splits the city into discrete dispatching regions and uses the reinforcement learning (RL) algorithm to dispatch vehicles in these regions. We also consider the mutual information (MI) between vehicle and request distribution as the intrinsic reward of the RL algorithm to improve the correlation between their distributions, thus ensuring the possibility of getting a ride for unusually distributed requests. In experimental results on a real-world taxi dataset, we demonstrate that our framework can significantly increase revenue up to an average of 3% over the existing best on-demand ride pooling method. ∗Corresponding author This work is licensed under a Creative Commons Attribution International 4. 0 License. Proc. of the 23rd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2024), N. Alechina, V. Dignum, M. Dastani, J. S. Sichman (eds.), May 6 – 10, 2024, Auckland, New Zealand. © 2024 International Foundation for Autonomous Agents and Multiagent Systems (www. ifaamas. org).

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AAAI Conference 2023 Conference Paper

Superpoint Transformer for 3D Scene Instance Segmentation

Jiahao Sun
Chunmei Qing
Junpeng Tan
Xiangmin Xu

Most existing methods realize 3D instance segmentation by extending those models used for 3D object detection or 3D semantic segmentation. However, these non-straightforward methods suffer from two drawbacks: 1) Imprecise bounding boxes or unsatisfactory semantic predictions limit the performance of the overall 3D instance segmentation framework. 2) Existing method requires a time-consuming intermediate step of aggregation. To address these issues, this paper proposes a novel end-to-end 3D instance segmentation method based on Superpoint Transformer, named as SPFormer. It groups potential features from point clouds into superpoints, and directly predicts instances through query vectors without relying on the results of object detection or semantic segmentation. The key step in this framework is a novel query decoder with transformers that can capture the instance information through the superpoint cross-attention mechanism and generate the superpoint masks of the instances. Through bipartite matching based on superpoint masks, SPFormer can implement the network training without the intermediate aggregation step, which accelerates the network. Extensive experiments on ScanNetv2 and S3DIS benchmarks verify that our method is concise yet efficient. Notably, SPFormer exceeds compared state-of-the-art methods by 4.3% on ScanNetv2 hidden test set in terms of mAP and keeps fast inference speed (247ms per frame) simultaneously. Code is available at https://github.com/sunjiahao1999/SPFormer.

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