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Xuefeng Liu

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19 papers

2 author rows

EAAI Journal 2026 Journal Article

Connectivity-aware three-dimensional fracture segmentation method for core computed tomography images

Xiangxin Zhao
Xin Wang
Liguo Niu
Xintao Mu
Xuefeng Liu

Accurately extracting the fracture structures from three-dimensional (3D) computed tomography (CT) images is essential for simulating and analyzing the physical properties of digital rocks. However, the heterogeneity within the rocks makes it difficult for threshold-based methods to identify blurred fracture boundaries. Furthermore, fractures have a complex spatial topological structure, resulting in existing slice-based segmentation methods ineffective in capturing spatial connectivity information. To address the above problems, a novel fracture segmentation method for 3D core CT images is proposed in this study. Firstly, we introduced a 3D multi-layer Transformer(3D-MLT) network to capture long-range dependence information and pixel spatial continuity features between adjacent layers. Then, we fed three axial slices into a two-dimensional (2D) multi-layer Transformer(2D-MLT) network to extract anisotropic features from multi-views. Subsequently, these features are fed into the Gradient Boosting Decision Tree (GBDT) module, which is iteratively enhanced by weaker learners to obtain preliminary segmentation probability maps. To correct the contribution of these maps to the segmentation results, we add dynamic weights to each of them and adjust it by backpropagation of the loss function. Finally, a multi-scale context-aware fusion(MSCAF) module fused spatial continuity features with these maps to obtain segmentation results. We compare it with other state-of-the-art(SOTA) methods and the experiment results demonstrate the superiority of our method in spatial structure connectivity of fracture.

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JBHI Journal 2026 Journal Article

FourierMask: Explain EEG-Based End-to-End Deep Learning Models in the Frequency Domain

Hanqi Wang
Jingyu Zhang
Kun Yang
Jichuan Xiong
Xuefeng Liu
Tao Chen
Liang Song

The rise of EEG-based end-to-end deep learning models has underscored the need to elucidate how these models process time-series raw EEG signals to generate predictions. The frequency domain provides a more suitable perspective for this task due to two key advantages: the strong correlation with cognitive states and the inherent capacity to model long-range temporal dependencies. However, this perspective remains underexplored in existing research. To bridge this gap, we propose FourierMask, the first mask perturbation framework specifically designed for frequency-domain explanation of EEG-based end-to-end models. Our method introduces three key innovations. First, the Fourier-based domain transformation enables direct manipulation of spectral components. Second, A learnable mask mechanism jointly models the spectral-spatial couplings relationship for EEG explanation. Third, a perturbation generator constrained by a target alignment loss ensures natural perturbations by minimizing distribution shift via cluster-aware regularization. We validate our method through experiments on an EEG benchmark dataset across EEGNet, TSCeption, and DeepConvNet models. Our method reaches a 36. 0% average accuracy drop gap (vs. 8. 6% for LIME and 6. 6% for easyPEASI) at the group-level. And, it reaches a 17. 8% average accuracy drop gap (vs. 8. 9% for LIME and 9. 9% for easyPEASI) at the instance-level. Our model-agnostic framework provides a plug-and-play solution for enhancing transparency of EEG-based end-to-end deep learning models. It links model decisions to frequency biomarkers, with potential applications in neuromedicine and brain-computer interfaces.

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AAAI Conference 2026 Conference Paper

Learning to Optimize Job Shop Scheduling Under Structural Uncertainty

Rui Zhang
Jianwei Niu
Xuefeng Liu
Shaojie Tang
Jing Yuan

The Job-Shop Scheduling Problem (JSSP), under various forms of manufacturing uncertainty, has recently attracted considerable research attention. Most existing studies focus on parameter uncertainty, such as variable processing times, and typically adopt the actor-critic framework. In this paper, we explore a different but prevalent form of uncertainty in JSSP: structural uncertainty. Structural uncertainty arises when a job may follow one of several routing paths, and the selection is determined not by policy, but by situational factors (e.g., the quality of intermediate products) that cannot be known in advance. Existing methods struggle to address this challenge due to incorrect credit assignment: a high-quality action may be unfairly penalized if it is followed by a time-consuming path. To address this problem, we propose a novel method named UP-AAC. In contrast to conventional actor-critic methods, UP-AAC employs an asymmetric architecture. While its actor receives a standard stochastic state, the critic is crucially provided with a deterministic state reconstructed in hindsight. This design allows the critic to learn a more accurate value function, which in turn provides a lower-variance policy gradient to the actor, leading to more stable learning. In addition, we design an attention-based Uncertainty Perception Model (UPM) to enhance the actor's scheduling decisions. Extensive experiments demonstrate that our method outperforms existing approaches in reducing makespan on benchmark instances.

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AAMAS Conference 2026 Conference Paper

Multi-Objective Coverage via Constraint Active Search

Zakaria Shams Siam
Xuefeng Liu
Chong Liu

Inthispaper, weformulatethenewmulti-objectivecoverage(MOC) problem where our goal is to identify a small set of representative samples whose predicted outcomes broadly cover the feasible multiobjectivespace. Thisproblemisofgreatimportanceinmanycritical real-worldapplications, e. g. ,drugdiscoveryandmaterialsdesign, as this representative set can be evaluated much faster than the whole feasible set, thus significantly accelerating the scientific discovery process. Existing works cannot be directly applied as they either focus on sample space coverage or multi-objective optimization that targets the Pareto front. However, chemically diverse samples often yield identical objective profiles, and safety constraints are usually defined on the objectives. To solve this MOC problem, we propose a novel search algorithm, MOC-CAS, which employs an upper confidence bound-based acquisition function to select optimistic samples guided by Gaussian process posterior predictions. For enabling efficient optimization, we develop a smoothed relaxation of the hard feasibility test and derive an approximate optimizer. Compared to the competitive baselines, we show that our MOC-CAS empirically achieves superior performances across large-scale protein-target datasets for SARS-CoV-2 and cancer, each assessed on five objectives derived from SMILES-based features.

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AAAI Conference 2025 Conference Paper

DiffDVC: Accurate Event Detection for Dense Video Captioning via Diffusion Models

Wei Chen
Jianwei Niu
Xuefeng Liu
Zhendong Wang
Shaojie Tang
Guogang Zhu

Dense video captioning (DVC) aims to describe multiple events within a video, and its performance is greatly affected by the accuracy of video event detection. Video event detection involves predicting the proposal boundaries (start and end times) and the classification score of each event in a video. Recently, a few methods have applied diffusion models originally designed for image object detection to detect events in DVC. These methods add noise to the ground-truth event proposal boundaries, and subsequently learn the denoising process. However, these methods often overlook the fundamental differences between videos and images. We observe that, whereas in images the important information for object classification is normally around the boundaries of the ground-truth boxes, in videos the key information for event classification is typically centered in the middle of ground-truth event proposals. As a result, the classification module in these existing diffusion models becomes insensitive to boundary changes introduced by the added noise, leading to sub-optimal performance. This paper introduces DiffDVC, an innovative diffusion model for DVC. The core of DiffDVC is a boundary-sensitive detector. The detector increases the sensitivity of the classification module to boundary changes by focusing on frames within a specific range around the start and end times of noisy event proposals. Additionally, this range is dynamically adjusted to suit different event proposals. Comprehensive experiments on ActivityNet-1.3, ActivityNet Captions, and YouCook2 datasets show DiffDVC achieving superior performance.

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EAAI Journal 2025 Journal Article

Forecasting stock market time series through the integration of bee colony optimizer and multivariate empirical mode decomposition with extreme gradient boosting regression

Xuefeng Liu
Zhixin Wu
Jiayue Xin

Stock price prediction is essential for the optimization of investment strategies, the mitigation of risks, and the facilitation of informed decision-making. Accurate forecasting is exceedingly difficult due to the nonlinear, nonstationary, and volatile nature of stock prices. This complexity is frequently not adequately addressed by conventional methods, underscoring the necessity of sophisticated hybrid models. This study uses stock price data from the Standard & Poor's 500 Index to develop a novel hybrid model, Multivariate Empirical Mode Decomposition-Artificial Bee Colony-Extreme Gradient Boosting Regression. Extreme Gradient Boosting Regression captures intricate patterns in the data, Artificial Bee Colony optimizes the hyperparameters of Extreme Gradient Boosting Regression to enhance model robustness, and Multivariate Empirical Mode Decomposition decomposes complex financial time-series data into manageable intrinsic mode functions. Close price, Momentum, Simple Moving Average, Moving Average Convergence Divergence, Relative Strength Index, and Trading volume comprise the dataset. These features are indispensable for identifying both short-term fluctuations and long-term trends. The presented model is significantly more effective than traditional models, as evidenced by its test set coefficient of determination of 0. 9914. The proposed model's robustness is confirmed by comprehensive 5-fold cross-validation and ablation studies, which also emphasize the significance of its integrated components. Furthermore, the model's adaptability is further illustrated by its ability to generalize to other markets, as evidenced by its coefficient of determination values exceeding 0. 99 on three other indexes. These results underscore the potential of artificial intelligence-driven hybrid models to enhance stock price forecasting, offering useful insights for policymakers, financial analysts, and investors.

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ICLR Conference 2025 Conference Paper

Learning from Imperfect Human Feedback: A Tale from Corruption-Robust Dueling

Yuwei Cheng
Fan Yao 0002
Xuefeng Liu
Haifeng Xu

This paper studies Learning from Imperfect Human Feedback (LIHF), addressing the potential irrationality or imperfect perception when learning from comparative human feedback. Building on evidences that human's imperfection decays over time (i.e., humans learn to improve), we cast this problem as a concave-utility continuous-action dueling bandit but under a restricted form of corruption: i.e., the corruption scale is decaying over time as $t^{\rho-1}$ for some ``imperfection rate'' $\rho \in [0, 1]$. With $T$ as the total number of iterations, we establish a regret lower bound of $ \Omega(\max\{\sqrt{T}, T^{\rho}\})$ for LIHF, even when $\rho$ is known. For the same setting, we develop the Robustified Stochastic Mirror Descent for Imperfect Dueling (RoSMID) algorithm, which achieves nearly optimal regret $\tilde{\mathcal{O}}(\max\{\sqrt{T}, T^{\rho}\})$. Core to our analysis is a novel framework for analyzing gradient-based algorithms for dueling bandit under corruption, and we demonstrate its general applicability by showing how this framework can be easily applied to obtain corruption-robust guarantees for other popular gradient-based dueling bandit algorithms. Our theoretical results are validated by extensive experiments.

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AAAI Conference 2025 Conference Paper

OpenVIS: Open-vocabulary Video Instance Segmentation

Pinxue Guo
Hao Huang
Peiyang He
Xuefeng Liu
Tianjun Xiao
Wenqiang Zhang

Open-vocabulary Video Instance Segmentation (OpenVIS) can simultaneously detect, segment, and track arbitrary object categories in a video, without being constrained to categories seen during training. In this work, we propose InstFormer, a carefully designed framework for the OpenVIS task that achieves powerful open-vocabulary capabilities through lightweight fine-tuning with limited-category data. InstFormer begins with the open-world mask proposal network, encouraged to propose all potential instance class-agnostic masks by the contrastive instance margin loss. Next, we introduce InstCLIP, adapted from pre-trained CLIP with Instance Guidance Attention, which encodes open-vocabulary instance tokens efficiently. These instance tokens not only enable open-vocabulary classification but also offer strong universal tracking capabilities. Furthermore, to prevent the tracking module from being constrained by the training data with limited categories, we propose the universal rollout association, which transforms the tracking problem into predicting the next frame’s instance tracking token. The experimental results demonstrate the proposed InstFormer achieve state-of-the-art capabilities on a comprehensive OpenVIS evaluation benchmark, while also achieves competitive performance in fully supervised VIS task.

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NeurIPS Conference 2025 Conference Paper

Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformation

Xinghao Wu
Xuefeng Liu
Jianwei Niu
Guogang Zhu
Mingjia Shi
Shaojie Tang
Jing Yuan

Federated Learning (FL) faces challenges due to data heterogeneity, which limits the global model’s performance across diverse client distributions. Personalized Federated Learning (PFL) addresses this by enabling each client to process an individual model adapted to its local distribution. Many existing methods assume that certain global model parameters are difficult to train effectively in a collaborative manner under heterogeneous data. Consequently, they localize or fine-tune these parameters to obtain personalized models. In this paper, we reveal that both the feature extractor and classifier of the global model are inherently strong, and the primary cause of its suboptimal performance is the mismatch between local features and the global classifier. Although existing methods alleviate this mismatch to some extent and improve performance, we find that they either (1) fail to fully resolve the mismatch while degrading the feature extractor, or (2) address the mismatch only post-training, allowing it to persist during training. This increases inter-client gradient divergence, hinders model aggregation, and ultimately leaves the feature extractor suboptimal for client data. To address this issue, we propose FedPFT, a novel framework that resolves the mismatch during training using personalized prompts. These prompts, along with local features, are processed by a shared self-attention-based transformation module, ensuring alignment with the global classifier. Additionally, this prompt-driven approach offers strong flexibility, enabling task-specific prompts to incorporate additional training objectives (\eg, contrastive learning) to further enhance the feature extractor. Extensive experiments show that FedPFT outperforms state-of-the-art methods by up to 5. 07%, with further gains of up to 7. 08% when collaborative contrastive learning is incorporated.

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IJCAI Conference 2024 Conference Paper

BeyondVision: An EMG-driven Micro Hand Gesture Recognition Based on Dynamic Segmentation

Nana Wang
Jianwei Niu
Xuefeng Liu
Dongqin Yu
Guogang Zhu
Xinghao Wu
Mingliang Xu
Hao Su

Hand gesture recognition (HGR) plays a pivotal role in natural and intuitive human-computer interactions. Recent HGR methods focus on recognizing gestures from vision-based images or videos. However, vision-based methods are limited in recognizing micro hand gestures (MHGs) (e. g. , pinch within 1cm) and gestures with occluded fingers. To address these issues, combined with the electromyography (EMG) technique, we propose BeyondVision, an EMG-driven MHG recognition system based on deep learning. BeyondVision consists of a wristband-style EMG sampling device and a tailored lightweight neural network BV-Net that can accurately translate EMG signals of MHGs to control commands in real-time. Moreover, we propose a post-processing mechanism and a weight segmentation algorithm to effectively improve the accuracy rate of MHG recognition. Subjective and objective experimental results show that our approach achieves over 95% average recognition rate, 2000Hz sampling frequency, and real-time micro gesture recognition. Our technique has been applied in a commercially available product, introduced at: https: //github. com/tyc333/NoBarriers.

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ICLR Conference 2024 Conference Paper

Blending Imitation and Reinforcement Learning for Robust Policy Improvement

Xuefeng Liu
Takuma Yoneda
Rick L. Stevens
Matthew R. Walter
Yuxin Chen 0001

While reinforcement learning (RL) has shown promising performance, its sample complexity continues to be a substantial hurdle, restricting its broader application across a variety of domains. Imitation learning (IL) utilizes oracles to improve sample efficiency, yet it is often constrained by the quality of the oracles deployed. To address the demand for robust policy improvement in real-world scenarios, we introduce a novel algorithm, Robust Policy Improvement (RPI), which actively interleaves between IL and RL based on an online estimate of their performance. RPI draws on the strengths of IL, using oracle queries to facilitate exploration—an aspect that is notably challenging in sparse-reward RL—particularly during the early stages of learning. As learning unfolds, RPI gradually transitions to RL, effectively treating the learned policy as an improved oracle. This algorithm is capable of learning from and improving upon a diverse set of black-box oracles. Integral to RPI are Robust Active Policy Selection (RAPS) and Robust Policy Gradient (RPG), both of which reason over whether to perform state-wise imitation from the oracles or learn from its own value function when the learner’s performance surpasses that of the oracles in a specific state. Empirical evaluations and theoretical analysis validate that RPI excels in comparison to existing state-of-the-art methodologies, demonstrating superior performance across various benchmark domains.

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NeurIPS Conference 2024 Conference Paper

Contextual Active Model Selection

Xuefeng Liu
Fangfang Xia
Rick L. Stevens
Yuxin Chen

While training models and labeling data are resource-intensive, a wealth of pre-trained models and unlabeled data exists. To effectively utilize these resources, we present an approach to actively select pre-trained models while minimizing labeling costs. We frame this as an online contextual active model selection problem: At each round, the learner receives an unlabeled data point as a context. The objective is to adaptively select the best model to make a prediction while limiting label requests. To tackle this problem, we propose CAMS, a contextual active model selection algorithm that relies on two novel components: (1) a contextual model selection mechanism, which leverages context information to make informed decisions about which model is likely to perform best for a given context, and (2)an active query component, which strategically chooses when to request labels for data points, minimizing the overall labeling cost. We provide rigorous theoretical analysis for the regret and query complexity under both adversarial and stochastic settings. Furthermore, we demonstrate the effectiveness of our algorithm on a diverse collection of benchmark classification tasks. Notably, CAMS requires substantially less labeling effort (less than 10%) compared to existing methods on CIFAR10 and DRIFT benchmarks, while achieving similar or better accuracy.

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ICML Conference 2024 Conference Paper

Entropy-Reinforced Planning with Large Language Models for Drug Discovery

Xuefeng Liu
Chih-chan Tien
Peng Ding
Songhao Jiang
Rick L. Stevens

The objective of drug discovery is to identify chemical compounds that possess specific pharmaceutical properties toward a binding target. Existing large language models (LLMS) can achieve high token matching scores in terms of likelihood for molecule generation. However, relying solely on LLM decoding often results in the generation of molecules that are either invalid due to a single misused token, or suboptimal due to unbalanced exploration and exploitation as a consequence of the LLM’s prior experience. Here we propose ERP, Entropy-Reinforced Planning for Transformer Decoding, which employs an entropy-reinforced planning algorithm to enhance the Transformer decoding process and strike a balance between exploitation and exploration. ERP aims to achieve improvements in multiple properties compared to direct sampling from the Transformer. We evaluated ERP on the SARS-CoV-2 virus (3CLPro) and human cancer cell target protein (RTCB) benchmarks and demonstrated that, in both benchmarks, ERP consistently outperforms the current state-of-the-art algorithm by 1-5 percent, and baselines by 5-10 percent, respectively. Moreover, such improvement is robust across Transformer models trained with different objectives. Finally, to further illustrate the capabilities of ERP, we tested our algorithm on three code generation benchmarks and outperformed the current state-of-the-art approach as well. Our code is publicly available at: https: //github. com/xuefeng-cs/ERP.

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IJCAI Conference 2024 Conference Paper

Estimating before Debiasing: A Bayesian Approach to Detaching Prior Bias in Federated Semi-Supervised Learning

Guogang Zhu
Xuefeng Liu
Xinghao Wu
Shaojie Tang
Chao Tang
Jianwei Niu
Hao Su

Federated Semi-Supervised Learning (FSSL) leverages both labeled and unlabeled data on clients to collaboratively train a model. In FSSL, the heterogeneous data can introduce prediction bias into the model, causing the model's prediction to skew towards some certain classes. Existing FSSL methods primarily tackle this issue by enhancing consistency in model parameters or outputs. However, as the models themselves are biased, merely constraining their consistency is not sufficient to alleviate prediction bias. In this paper, we explore this bias from a Bayesian perspective and demonstrate that it principally originates from label prior bias within the training data. Building upon this insight, we propose a debiasing method for FSSL named FedDB. FedDB utilizes the Average Prediction Probability of Unlabeled Data (APP-U) to approximate the biased prior. During local training, FedDB employs APP-U to refine pseudo-labeling through Bayes' theorem, thereby significantly reducing the label prior bias. Concurrently, during the model aggregation, FedDB uses APP-U from participating clients to formulate unbiased aggregate weights, thereby effectively diminishing bias in the global model. Experimental results show that FedDB can surpass existing FSSL methods. The code is available at https: //github. com/GuogangZhu/FedDB.

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NeurIPS Conference 2024 Conference Paper

Why Go Full? Elevating Federated Learning Through Partial Network Updates

Haolin Wang
Xuefeng Liu
Jianwei Niu
Wenkai Guo
Shaojie Tang

Federated learning is a distributed machine learning paradigm designed to protect user data privacy, which has been successfully implemented across various scenarios. In traditional federated learning, the entire parameter set of local models is updated and averaged in each training round. Although this full network update method maximizes knowledge acquisition and sharing for each model layer, it prevents the layers of the global model from cooperating effectively to complete the tasks of each client, a challenge we refer to as layer mismatch. This mismatch problem recurs after every parameter averaging, consequently slowing down model convergence and degrading overall performance. To address the layer mismatch issue, we introduce the FedPart method, which restricts model updates to either a single layer or a few layers during each communication round. Furthermore, to maintain the efficiency of knowledge acquisition and sharing, we develop several strategies to select trainable layers in each round, including sequential updating and multi-round cycle training. Through both theoretical analysis and experiments, our findings demonstrate that the FedPart method significantly surpasses conventional full network update strategies in terms of convergence speed and accuracy, while also reducing communication and computational overheads.

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ICML Conference 2023 Conference Paper

Active Policy Improvement from Multiple Black-box Oracles

Xuefeng Liu
Takuma Yoneda
Chaoqi Wang
Matthew R. Walter
Yuxin Chen 0001

Reinforcement learning (RL) has made significant strides in various complex domains. However, identifying an effective policy via RL often necessitates extensive exploration. Imitation learning aims to mitigate this issue by using expert demonstrations to guide exploration. In real-world scenarios, one often has access to multiple suboptimal black-box experts, rather than a single optimal oracle. These experts do not universally outperform each other across all states, presenting a challenge in actively deciding which oracle to use and in which state. We introduce MAPS and MAPS-SE, a class of policy improvement algorithms that perform imitation learning from multiple suboptimal oracles. In particular, MAPS actively selects which of the oracles to imitate and improve their value function estimates, and MAPS-SE additionally leverages an active state exploration criterion to determine which states one should explore. We provide a comprehensive theoretical analysis and demonstrate that MAPS and MAPS-SE enjoy sample efficiency advantage over the state-of-the-art policy improvement algorithms. Empirical results show that MAPS-SE significantly accelerates policy optimization via state-wise imitation learning from multiple oracles across a broad spectrum of control tasks in the DeepMind Control Suite.

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AAAI Conference 2021 Conference Paper

MangaGAN: Unpaired Photo-to-Manga Translation Based on The Methodology of Manga Drawing

Hao Su
Jianwei Niu
Xuefeng Liu
Qingfeng Li
Jiahe Cui
Ji Wan

Manga is a world popular comic form originated in Japan, which typically employs black-and-white stroke lines and geometric exaggeration to describe humans’ appearances, poses, and actions. In this paper, we propose MangaGAN, the first method based on Generative Adversarial Network (GAN) for unpaired photo-to-manga translation. Inspired by the drawing process of experienced manga artists, Manga- GAN generates geometric features and converts each facial region into the manga domain with a tailored multi-GANs architecture. For training MangaGAN, we collect a new dataset from a popular manga work with extensive features. To produce high-quality manga faces, we propose a structural smoothing loss to smooth stroke-lines and avoid noisy pixels, and a similarity preserving module to improve the similarity between domains of photo and manga. Extensive experiments show that MangaGAN can produce high-quality manga faces preserving both the facial similarity and manga style, and outperforms other reference methods.

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IJCAI Conference 2020 Conference Paper

SelectScale: Mining More Patterns from Images via Selective and Soft Dropout

Zhengsu Chen
Jianwei Niu
Xuefeng Liu
Shaojie Tang

Convolutional neural networks (CNNs) have achieved remarkable success in image recognition. Although the internal patterns of the input images are effectively learned by the CNNs, these patterns only constitute a small proportion of useful patterns contained in the input images. This can be attributed to the fact that the CNNs will stop learning if the learned patterns are enough to make a correct classification. Network regularization methods like dropout and SpatialDropout can ease this problem. During training, they randomly drop the features. These dropout methods, in essence, change the patterns learned by the networks, and in turn, forces the networks to learn other patterns to make the correct classification. However, the above methods have an important drawback. Randomly dropping features is generally inefficient and can introduce unnecessary noise. To tackle this problem, we propose SelectScale. Instead of randomly dropping units, SelectScale selects the important features in networks and adjusts them during training. Using SelectScale, we improve the performance of CNNs on CIFAR and ImageNet.

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TIST Journal 2017 Journal Article

DMAD

Jiaxing Shen
Jiannong Cao
Xuefeng Liu
Chisheng Zhang

Wireless networks offer many advantages over wired local area networks such as scalability and mobility. Strategically deployed wireless networks can achieve multiple objectives like traffic offloading, network coverage, and indoor localization. To this end, various mathematical models and optimization algorithms have been proposed to find optimal deployments of access points (APs). However, wireless signals can be blocked by the human body, especially in crowded urban spaces. As a result, the real coverage of an on-site AP deployment may shrink to some degree and lead to unexpected dead spots (areas without wireless coverage). Dead spots are undesirable, since they degrade the user experience in network service continuity, on one hand, and, on the other hand paralyze some applications and services like tracking and monitoring when users are in these areas. Nevertheless, it is nontrivial for existing methods to analyze the impact of human beings on wireless coverage. Site surveys are too time consuming and labor intensive to conduct. It is also infeasible for simulation methods to predict the number of on-site people. In this article, we propose DMAD, a Data-driven Measuring of Wi-Fi Access point Deployment, which not only estimates potential dead spots of an on-site AP deployment but also quantifies their severity, using simple Wi-Fi data collected from the on-site deployment and shop profiles from the Internet. DMAD first classifies static devices and mobile devices with a decision-tree classifier. Then it locates mobile devices to grid-level locations based on shop popularities, wireless signal, and visit duration. Last, DMAD estimates the probability of dead spots for each grid during different time slots and derives their severity considering the probability and the number of potential users. The analysis of Wi-Fi data from static devices indicates that the Pearson Correlation Coefficient of wireless coverage status and the number of on-site people is over 0.7, which confirms that human beings may have a significant impact on wireless coverage. We also conduct extensive experiments in a large shopping mall in Shenzhen. The evaluation results demonstrate that DMAD can find around 70% of dead spots with a precision of over 70%.

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