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Bing Zhou

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10 papers
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10

JBHI Journal 2026 Journal Article

Pseudo Anomalies and Hard Sample Mining for Ventricular Arrhythmia Anomaly Detection

  • Haoyi Fan
  • Shaochen Gao
  • Han Han
  • Mingzhe Han
  • Bing Zhou
  • Zongmin Wang

Ventricular arrhythmias (VA) are among the most prevalent and clinically significant cardiac arrhythmias. Conventional detection methodologies predominantly employ supervised learning approaches that depend on precisely annotated training datasets. However, the morphological similarity between VA waveforms and noise artifacts poses a significant challenge for traditional algorithms in discriminating these clinically distinct categories. In this paper, we propose a novel anomaly detection framework named PHVA ( P seudo-data and H ard-sample mining for VA detection) based on one-class anomaly detection, where only normal ECGs are used for training, while pseudo-anomalies are generated through self-supervised modules. These modules, including pseudo-anomaly generation and hard-sample mining, provide supervisory signals without requiring abnormal labels. Specifically, we introduce a physiology-aware synthetic ECG generation method that captures the beat morphology and waveform characteristics of VA while incorporating realistic noise simulations based on conventional noise models. These pseudo-annotated signals are then used to refine the decision boundary of a time-frequency hypersphere, constructed from normal ECG features in both temporal and spectral domains. Additionally, we employ triplet-loss-based hard-sample mining to improve the model's discriminative power for ventricular fibrillation detection. Extensive experiments on three public ECG datasets demonstrate that our proposed method PHVA achieves superior overall performance, outperforming state-of-the-art anomaly detection baselines by up to 8. 7% in AUC (the Area Under a Receiver Operating Characteristic Curve). We share an online repository containing the code and data processing scripts https://github.com/Augustgaoshaochen/PHVA

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EAAI Journal 2025 Journal Article

A camouflage target classification method based on spectral difference enhancement and pixel-pair features in land-based hyperspectral images

  • Jiale Zhao
  • Dan Fang
  • Jiaju Ying
  • Yudan Chen
  • Qi Chen
  • Qianghui Wang
  • Guanglong Wang
  • Bing Zhou

Hyperspectral images are capable of capturing rich spatial and spectral information of targets, rendering them particularly valuable for camouflage target detection and classification applications. However, as camouflage technologies continue to advance, the spectral similarity between camouflaged targets and their backgrounds has become increasingly pronounced, presenting significant challenges for camouflaged target classification in hyperspectral imagery. To overcome this challenge, this paper introduces a novel land-based hyperspectral image classification approach for camouflaged targets, termed Spectral Difference Enhancement and Pixel-Pair Features (SDE-PPF). The proposed methodology initially conducts spectral rearrangement of the hyperspectral image based on target spectral characteristics, which induces oscillatory patterns in the background spectrum. Subsequently, first-order spectral differentiation coupled with nonlinear processing is applied to the rearranged hyperspectral image to effectively amplify subtle spectral differences between targets and backgrounds, thereby improving their discriminability. Following spectral enhancement, the method constructs pixel pairs from the processed hyperspectral image and employs a convolutional neural network to extract pixel-pair features. Network parameters are optimized through comprehensive analysis of pixel-pair sample relationships. During testing, the central pixel is systematically paired with its neighboring pixels, and classification is performed using the trained model. Ultimately, the final classification of each central pixel is determined through a voting mechanism that consolidates all classification results. Comprehensive experiments were performed on four distinct land-based hyperspectral image datasets containing camouflage targets. The experimental results demonstrate that the proposed SDE-PPF method outperforms conventional hyperspectral image classification approaches, achieving remarkable average classification accuracies of 98. 46 %, 99. 05 %, 98. 94 %, and 99. 21 % for detecting camouflage targets against grassland, barren Grassland, withered leaf, and shrubbery backgrounds, respectively. This innovative approach establishes an effective and robust technical solution for camouflage target classification and detection, exhibiting considerable potential for diverse practical applications.

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NeurIPS Conference 2025 Conference Paper

SnapMoGen: Human Motion Generation from Expressive Texts

  • Chuan Guo
  • Inwoo Hwang
  • Jian Wang
  • Bing Zhou

Text-to-motion generation has experienced remarkable progress in recent years. However, current approaches remain limited to synthesizing motion from short or general text prompts, primarily due to dataset constraints. This limitation undermines fine-grained controllability and generalization to unseen prompts. In this paper, we introduce SnapMoGen, a new text-motion dataset featuring high-quality motion capture data paired with accurate, \textit{expressive} textual annotations. The dataset comprises 20K motion clips totaling 44 hours, accompanied by 122 detailed textual descriptions averaging 48 words per description (vs. 12 words of HumanML3D). Importantly, these motion clips preserve original temporal continuity as they were in long sequences, facilitating research in long-term motion generation and blending. We also improve upon previous generative masked modeling approaches. Our model, MoMask++, transforms motion into \textbf{multi-scale} token sequences that better exploit the token capacity, and learns to generate all tokens using a single generative masked transformer. MoMask++ achieves state-of-the-art performance on both HumanML3D and OmniMotion benchmarks. Additionally, we demonstrate the ability to process casual user prompts by employing an LLM to reformat inputs to align with the expressivity and narration style of SnapMoGen.

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EAAI Journal 2025 Journal Article

Workload-based adaptive decision-making for edge server layout with deep reinforcement learning

  • Shihua Li
  • Yanjie Zhou
  • Bing Zhou
  • Zongmin Wang

Mobile edge computing (MEC) is crucial in applications such as intelligent transportation, innovative healthcare, and smart cities. By deploying servers with computing and storage capabilities at the network edge, MEC enables low-latency services close to end users. However, the configuration of edge servers needs to meet the low-latency requirements and effectively balance the servers’ workloads. This paper proposes an adaptive layout and dynamic optimization method, modeling the edge server layout problem as a Markov decision process. It introduces a workload-based server placement rule that adjusts the locations of edge servers according to the load of base stations, enabling the learning of low-latency and load-balanced server layout strategies. Experimental validation on a real dataset from Shanghai Telecom shows that the proposed algorithm improves average latency performance by about 40% compared to existing technologies, and enhances workload balancing performance by about 17%.

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AAMAS Conference 2024 Conference Paper

Competitive Analysis of Online Facility Open Problem

  • Binghan Wu
  • Wei Bao
  • Bing Zhou

We investigate an online cost minimization problem of serving requests in a tree of facilities, referred to as the Online Facility Open Problem (Online FOP). To address this problem, we propose the Anchor-Barrier Algorithm (ABA), a threshold-based algorithm applicable to any tree and any cost assignment, which can work in a distributed manner for scalability. We conduct the competitive analysis and show that ABA’s achieves the optimal competitive ratio Height + 2, where Height is the height of the facility tree.

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EAAI Journal 2023 Journal Article

Multi-spatio-temporal Fusion Graph Recurrent Network for Traffic Forecasting

  • Wei Zhao
  • Shiqi Zhang
  • Bing Zhou
  • Bei Wang

Accurate traffic forecasting is crucial for smart city development in the new era. However, the intricate spatial and temporal dependencies in traffic data present significant challenges for prediction accuracy. Existing methods often rely on predefined adjacency matrices to capture Spatio-temporal dependencies, which may not adapt well to the dynamic nature of road traffic. To address these challenges, we propose the Multi-Spatio-temporal Fusion Graph Recurrent Network (MSTFGRN). This innovative approach introduces a data-driven method for generating a weighted adjacency matrix, effectively capturing real-time spatial dependencies that are not adequately captured by predefined matrices. The MSTFGRN also incorporates a novel two-way Spatio-temporal fusion operation to learn hidden dependencies between parallel Spatio-temporal relations at different time points. Additionally, a global attention mechanism is integrated into the Spatio-temporal fusion module, enabling the simultaneous capture of global Spatio-temporal dependencies. Through extensive trials on publicly available highway traffic datasets, our method demonstrates state-of-the-art performance compared to alternative baselines.

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IJCAI Conference 2022 Conference Paper

Competitive Analysis for Multi-Commodity Ski-Rental Problem

  • Binghan Wu
  • Wei Bao
  • Dong Yuan
  • Bing Zhou

We investigate an extended version of the classical ski-rental problem with multiple commodities. A customer uses a set of commodities altogether, and he/she needs to choose payment options to cover the usage of each commodity without the knowledge of the future. The payment options of each commodity include (1) renting: to pay for an on-demand usage and (2) buying: to pay for the lifetime usage. It is a novel extension of the classical ski-rental problem which deals with only one commodity. To address this problem, we propose a new online algorithm called the Multi-Object Break-Even (MOBE) algorithm and conduct competitive analysis. We show that the tight lower and upper bounds of MOBE algorithm's competitive ratio are e/e-1 and 2 respectively against adaptive adversary under arbitrary renting and buying prices. We further prove that MOBE algorithm is an optimal online algorithm if commodities have the same rent-to-buy ratio. Numerical results verify our theoretical conclusion and demonstrate the advantages of MOBE in a real-world scenario.

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ICAPS Conference 2021 Conference Paper

Online Hedge Reservation for Diverse Plans and Competitive Analysis

  • Binghan Wu
  • Wei Bao 0001
  • Dong Yuan 0001
  • Bing Zhou

In this paper, we investigate the plan reservation problem with diverse plans in mobile networks. The pricing scheme includes: 1) Pay-as-you-go (PAYG) payment; 2) All-in-one plan: an upfront fee is charged to cover data volume of a period of time; and 3) Directional plan: an upfront fee is charged to cover data volume of a specific app for a period of time. We investigate online plan reservation with competitive analysis, as the data volume is not known until an app is used. The problem is challenging as there are multiple directional plans and one all-in-one plan, creating a large decision space and complicated correlations among the decisions. We propose the Online Hedge Reservation (OHR) Algorithm to solve the problem and prove that it achieves e^beta/(e^beta-1) competitive ratio when each plan is valid till the end of each calendar month and 2e^beta/(e^beta-1) competitive ratio when each plan is valid for a full month, where beta is the ratio of prices of the directional plans and the all-in-one plan. This is an exciting neat extension of the competitive ratio e/(e-1) of the classic ski-rental problem. Finally, trace-driven simulation is conducted to further verify the advantages of the OHR Algorithm.

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IJCAI Conference 2020 Conference Paper

Semi-Dynamic Hypergraph Neural Network for 3D Pose Estimation

  • Shengyuan Liu
  • Pei Lv
  • Yuzhen Zhang
  • Jie Fu
  • Junjin Cheng
  • Wanqing Li
  • Bing Zhou
  • Mingliang Xu

This paper proposes a novel Semi-Dynamic Hypergraph Neural Network (SD-HNN) to estimate 3D human pose from a single image. SD-HNN adopts hypergraph to represent the human body to effectively exploit the kinematic constrains among adjacent and non-adjacent joints. Specifically, a pose hypergraph in SD-HNN has two components. One is a static hypergraph constructed according to the conventional tree body structure. The other is the semi-dynamic hypergraph representing the dynamic kinematic constrains among different joints. These two hypergraphs are combined together to be trained in an end-to-end fashion. Unlike traditional Graph Convolutional Networks (GCNs) that are based on a fixed tree structure, the SD-HNN can deal with ambiguity in human pose estimation. Experimental results demonstrate that the proposed method achieves state-of-the-art performance both on the Human3. 6M and MPI-INF-3DHP datasets.

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TIST Journal 2018 Journal Article

Traffic Simulation and Visual Verification in Smog

  • Mingliang Xu
  • Hua Wang
  • Shili Chu
  • Yong Gan
  • Xiaoheng Jiang
  • Yafei Li
  • Bing Zhou

Smog causes low visibility on the road and it can impact the safety of traffic. Modeling traffic in smog will have a significant impact on realistic traffic simulations. Most existing traffic models assume that drivers have optimal vision in the simulations, making these simulations are not suitable for modeling smog weather conditions. In this article, we introduce the Smog Full Velocity Difference Model (SMOG-FVDM) for a realistic simulation of traffic in smog weather conditions. In this model, we present a stadia model for drivers in smog conditions. We introduce it into a car-following traffic model using both psychological force and body force concepts, and then we introduce the SMOG-FVDM. Considering that there are lots of parameters in the SMOG-FVDM, we design a visual verification system based on SMOG-FVDM to arrive at an adequate solution which can show visual simulation results under different road scenarios and different degrees of smog by reconciling the parameters. Experimental results show that our model can give a realistic and efficient traffic simulation of smog weather conditions.

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v2026.09.13