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Yongjun Wang

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10 papers

2 author rows

IJCAI Conference 2025 Conference Paper

MiniMal: Hard-Label Adversarial Attack Against Static Malware Detection with Minimal Perturbation

Chengyi Li
Zhiyuan Jiang
Yongjun Wang
Tian Xia
Yayuan Zhang
Yuhang Mao

Static malware detectors based on machine learning are integral to contemporary antivirus systems, but they are vulnerable to adversarial attacks. While existing research has demonstrated success with adversarial attacks in black-box hard-label scenarios, challenges such as high perturbation rates and incomplete retention of functional integrity remain. To address these issues, we propose a novel black-box hard-label attack method, MiniMal. MiniMal begins with initialized adversarial examples and utilizes binary search and particle swarm optimization algorithms to streamline the perturbation content, significantly reducing the perturbation rate of the adversarial examples. Furthermore, we propose a functionality verification method grounded in file format parsing and control flow graph comparisons to ensure the functional integrity of the adversarial examples. Experimental results indicate that MiniMal achieves an attack success rate of over 98% against three leading machine learning detectors, improving performance by approximately 4. 8% to 7. 1% compared to state-of-the-art methods. MiniMal reduces perturbation rates to below 40%, making them 9 to 11 times lower than those of previous methods. Additionally, functional verification via Cuckoo Sandbox revealed that the adversarial examples generated by MiniMal retained 100% functional integrity, even with various modifications applied.

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AIIM Journal 2024 Journal Article

A clinically actionable and explainable real-time risk assessment framework for stroke-associated pneumonia

Lutao Dai
Xin Yang
Hao Li
Xingquan Zhao
Lin Lin
Yong Jiang
Yongjun Wang
Zixiao Li

The current medical practice is more responsive rather than proactive, despite the widely recognized value of early disease detection, including improving the quality of care and reducing medical costs. One of the cornerstones of early disease detection is clinically actionable predictions, where predictions are expected to be accurate, stable, real-time and interpretable. As an example, we used stroke-associated pneumonia (SAP), setting up a transformer-encoder-based model that analyzes highly heterogeneous electronic health records in real-time. The model was proven accurate and stable on an independent test set. In addition, it issued at least one warning for 98. 6 % of SAP patients, and on average, its alerts were ahead of physician diagnoses by 2. 71 days. We applied Integrated Gradient to glean the model's reasoning process. Supplementing the risk scores, the model highlighted critical historical events on patients' trajectories, which were shown to have high clinical relevance.

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EAAI Journal 2024 Journal Article

Enhancing large language model capabilities for rumor detection with Knowledge-Powered Prompting

Yeqing Yan
Peng Zheng
Yongjun Wang

Amid the proliferation of misinformation on social networks, automated rumor detection has emerged as a pivotal and pressing research domain. Nonetheless, current methodologies are hindered by constrained feature representations and limited adaptability in effectively addressing diverse and unconventional rumors. The incorporation of large-scale language models holds the promise of delivering heightened semantic comprehension and broader adaptability. Regrettably, prevailing general-purpose prompting approaches frequently fall short in furnishing adequate domain-specific context and guidance, thereby restricting their utility in the context of rumor detection. To ameliorate these concerns, we introduce the Knowledge-Powered Prompting strategy, which imparts task-relevant prompts and context to the model by amalgamating domain expertise with large-scale language models. This fusion equips the model to better align with the exigencies of rumor detection, mitigating the challenges posed by sensitivity to semantic subtleties and a paucity of training samples. In particular, we devise exploration prompts and bolster the prompt representation with a dynamic knowledge injection module, thereby facilitating profound reasoning about pivotal entities. Subsequently, we extract valuable external knowledge through the filtration of interactions between knowledge and claim, thereby diminishing the impact of noise. Concurrently, we undertake joint optimization, encompassing multi-task prompt population and categorical judgment objectives, fostering synergistic semantic modeling and discriminative assessments. Empirical evaluations reveal that our methodology substantially outperforms existing models.

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ICRA Conference 2024 Conference Paper

Learning Dual-arm Object Rearrangement for Cartesian Robots

Shishun Zhang
Qijin She
Wenhao Li
Chenyang Zhu 0002
Yongjun Wang
Ruizhen Hu
Kai Xu 0004

This work focuses on the dual-arm object rearrangement problem abstracted from a realistic industrial scenario of Cartesian robots. The goal of this problem is to transfer all the objects from sources to targets with the minimum total completion time. To achieve the goal, the core idea is to develop an effective object-to-arm task assignment strategy for minimizing the cumulative task execution time and maximizing the dual-arm cooperation efficiency. One of the difficulties in the task assignment is the scalability problem. As the number of objects increases, the computation time of traditional offline-search-based methods grows strongly for computational complexity. Encouraged by the adaptability of reinforcement learning (RL) in long-sequence task decisions, we propose an online task assignment decision method based on RL, and the computation time of our method only increases linearly with the number of objects. Further, we design an attention-based network to model the dependencies between the input states during the whole task execution process to help find the most reasonable object-to-arm correspondence in each task assignment round. In the experimental part, we adapt some search-based methods to this specific setting and compare our method with them. Experimental result shows that our approach achieves outperformance over search-based methods in total execution time and computational efficiency, and also verifies the generalization of our method to different numbers of objects. In addition, we show the effectiveness of our method deployed on the real robot in the supplementary video.

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YNIMG Journal 2024 Journal Article

Relationships between brain structure-function coupling in normal aging and cognition: A cross-ethnicity population-based study

Chang Liu
Jing Jing
Jiyang Jiang
Wei Wen
Wanlin Zhu
Zixiao Li
Yuesong Pan
Xueli Cai

Increased efforts in neuroscience seek to understand how macro-anatomical and physiological connectomes cooperatively work to generate cognitive behaviors. However, the structure-function coupling characteristics in normal aging individuals remain unclear. Here, we developed an index, the Coupling in Brain Structural connectome and Functional connectome (C-BSF) index, to quantify regional structure-function coupling in a large community-based cohort. C-BSF used diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (fMRI) data from the Polyvascular Evaluation for Cognitive Impairment and Vascular Events study (PRECISE) cohort (2007 individuals, age: 61.15 ± 6.49 years) and the Sydney Memory and Ageing Study (MAS) cohort (254 individuals, age: 83.45 ± 4.33 years). We observed that structure-function coupling was the strongest in the visual network and the weakest in the ventral attention network. We also observed that the weaker structure-function coupling was associated with increased age and worse cognitive level of the participant. Meanwhile, the structure-function coupling in the visual network was associated with the visuospatial performance and partially mediated the connections between age and the visuospatial function. This work contributes to our understanding of the underlying brain mechanisms by which aging affects cognition and also help establish early diagnosis and treatment approaches for neurological diseases in the elderly.

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EAAI Journal 2023 Journal Article

A graph-based pivotal semantic mining framework for rumor detection

Yeqing Yan
Yongjun Wang
Peng Zheng

As the gathering place for modern content sharing, social platforms not only provide places for people to obtain information and express opinions, but also provide hidden channels for the generation and dissemination of rumors. Rumor detection is an important and challenging task for mining fake information in social networks. Previous methods utilized sequential models to embed semantic features, which are not comprehensive for mining pivotal semantic information and easy to ignore discontinuous dependencies. However, excessive mining of information content will lead to the acquisition of some redundant information, whose existence will affect the extraction of useful features and the detection ability of the model. To address these issues, this paper offers a graph-based pivotal semantic mining framework. Specifically, we model the content information as a graph structure, learn the semantic dependencies across segments through a gated graph neural network, and co-learn by combining the propagation features of rumors. Furthermore, in order to highlight the precious essential semantic information, a shared unit cell is offered to reduce the influence of redundant information. Experimental results on realworld datasets show that the proposed method exceeds existing methods in terms of benchmark testing.

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YNICL Journal 2022 Journal Article

Thrombus magnetic susceptibility is associated with recanalization and clinical outcome in patients with ischemic stroke

Jie Chen
Zhe Zhang
Ximing Nie
Yuyuan Xu
Chunlei Liu
Xingquan Zhao
Zhongrong Miao
Yongjun Wang

In acute ischemic stroke patients with large vessel occlusion, the characteristics of the occluding thrombus on neuroimaging may be associated with recanalization after endovascular thrombectomy (EVT); however, the relationship between magnetic susceptibility of thrombus and clinical outcome remains unclear. We utilized quantitative susceptibility mapping (QSM) MRI to assess the magnetic susceptibility of thrombus in acute ischemic stroke patients undergoing EVT, and to evaluate its relationship with recanalization and functional outcomes. Patients with documented intracranial artery occlusion were consecutively recruited from one research center of the RESCUE-RE study (a registration study for Critical Care of Acute Ischemic Stroke After Recanalization). All the recruited patients underwent a 3D multi-echo MRI scan on a 3.0 T scanner for both susceptibility-weighted imaging (SWI) and QSM quantification of the thrombus. Among 61 patients included in the analyses, 51 (75.0 %) patients achieved thrombolysis in cerebral infarction (TICI) 2b/3 and 22 (36.1 %) patients had favorable functional outcomes. Successful recanalization was significantly associated with a higher thrombus magnetic susceptibility mean value (0.27 ± 0.09 vs 0.20 ± 0.09 ppm, p = 0.020) and lower coefficient of variation (0.42 ± 0.12 vs 0.52 ± 0.19, p = 0.024). ROC curve analysis showed the optimal cutoff value for thrombus susceptibility for predicting good clinical outcomes was 0.25 ppm (sensitivity 86.4 %, specificity 69.2 %). In multivariable logistic regression analyses, increased thrombus magnetic susceptibility was independently and significantly associated with good functional outcomes (adjusted odds ratio 15.11 [95 % confidence interval 2.64-86.46], p = 0.002). This study demonstrated that the increased thrombus magnetic susceptibility is associated with successful recanalization and favorable functional outcomes for intracranial artery occluded stroke patients.

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AAAI Conference 2019 Conference Paper

Embedding-Based Complex Feature Value Coupling Learning for Detecting Outliers in Non-IID Categorical Data

Hongzuo Xu
Yongjun Wang
Zhiyue Wu
Yijie Wang

Non-IID categorical data is ubiquitous and common in realworld applications. Learning various kinds of couplings has been proved to be a reliable measure when detecting outliers in such non-IID data. However, it is a critical yet challenging problem to model, represent, and utilise high-order complex value couplings. Existing outlier detection methods normally only focus on pairwise primary value couplings and fail to uncover real relations that hide in complex couplings, resulting in suboptimal and unstable performance. This paper introduces a novel unsupervised embedding-based complex value coupling learning framework EMAC and its instance SCAN to address these issues. SCAN first models primary value couplings. Then, coupling bias is defined to capture complex value couplings with different granularities and highlight the essence of outliers. An embedding method is performed on the value network constructed via biased value couplings, which further learns high-order complex value couplings and embeds these couplings into a value representation matrix. Bidirectional selective value coupling learning is proposed to show how to estimate value and object outlierness through value couplings. Substantial experiments show that SCAN (i) significantly outperforms five state-of-the-art outlier detection methods on thirteen real-world datasets; and (ii) has much better resilience to noise than its competitors.

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YNIMG Journal 2019 Journal Article

Reduction of cerebral blood flow in community-based adults with subclinical cerebrovascular atherosclerosis: A 3.0T magnetic resonance imaging study

Hualu Han
Runhua Zhang
Gaifen Liu
Huiyu Qiao
Zhensen Chen
Yang Liu
Xiaoyi Chen
Dongye Li

Reduction in cerebral blood flow (CBF), one of the major metrics for cerebral perfusion, is associated with many brain disorders. Therefore, early characterization of CBF prior to occurrence of symptoms is essential for prevention of cerebral ischemic events. We hypothesized that large artery atherosclerosis might be a potential indicator for decline in cerebral perfusion. The aim of this study was to investigate the relationship between large artery atherosclerosis and CBF in asymptomatic adults. A total of 134 asymptomatic subjects (mean age, 56. 2 ± 12. 8 years; 54 males) were recruited and underwent magnetic resonance (MR) imaging for brain and intracranial and extracranial carotid arteries. Presence or absence of cerebrovascular atherosclerosis was determined on MR vessel wall images. The CBF was measured with pseudo-continuous arterial spin labeling (pCASL) imaging. The CBF values in internal carotid artery (ICA) (37. 2 ± 5. 8 vs. 39. 0 ± 4. 9 ml/100 g/min, P = 0. 049) and vertebrobasilar artery (VA-BA) territories (42. 0 ± 6. 8 vs. 44. 8 ± 7. 0 ml/100 g/min, P = 0. 023) were significantly reduced in subjects with cerebrovascular plaque compared to those without. Presence of cerebrovascular plaque was significantly associated with CBF of VA-BA territory before (odds ratio, 2. 89; 95% confidence interval, 1. 37–6. 08; P = 0. 005) and after adjusted for confounding factors including age, gender, body-mass-index, diabetes, systolic blood pressure, hyperlipidemia and history of cardiovascular disease (odds ratio, 2. 76; 95% confidence interval, 1. 18–6. 46; P = 0. 019). In conclusion, presence of cerebrovascular atherosclerosis is independently associated with reduction in CBF measured by pCASL in asymptomatic adults, suggesting that cerebrovascular large artery atherosclerosis might be an effective indicator for impairment of cerebral microcirculation hemodynamics.

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YNIMG Journal 2018 Journal Article

Variation in longitudinal trajectories of cortical sulci in normal elderly

Xinke Shen
Tao Liu
Dacheng Tao
Yubo Fan
Jicong Zhang
Shuyu Li
Jiyang Jiang
Wanlin Zhu

Sulcal morphology has been reported to change with age-related neurological diseases, but the trajectories of sulcal change in normal ageing in the elderly is still unclear. We conducted a study of sulcal morphological changes over seven years in 132 normal elderly participants aged 70–90 years at baseline, and who remained cognitively normal for the next seven years. We examined the fold opening and sulcal depth of sixteen (eight on each hemisphere) prominent sulci based on T1-weighted MRI using automated methods with visual quality control. The trajectory of each individual sulcus with respect to age was examined separately by linear mixed models. Fold opening was best modelled by cubic fits in five sulci, by quadratic models in six sulci and by linear models in five sulci, indicating an accelerated widening of a number of sulci in older age. Sulcal depth showed significant linear decline in three sulci and quadratic trend in one sulcus. Turning points of non-linear trajectories towards accelerated widening of the fold were found to be around the age between 75 and 80, indicating an accelerated atrophy of brain cortex starting in the age of late 70s. Our findings of cortical sulcal changes in normal ageing could provide a reference for studies of neurocognitive disorders, including neurodegenerative diseases, in the elderly.

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