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Jin Yang

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8 papers

2 author rows

YNIMG Journal 2026 Journal Article

Inhibitory control in problematic usage of the internet: An ALE meta-analysis

Xiaoyi Li
Jin Yang
Ofir Turel
Shuyue Zhang
Qinghua He

Problematic Usage of the Internet (PUI) is often characterized by deficits in inhibitory control. The links between such deficits and altered brain activity, though, have been fragmented and mixed. Thus, we seek here to identify communal patterns of brain activation related to inhibitory control in individuals with PUI through a comprehensive quantitative synthesis. To this end, we performed a systematic search of PubMed and Web of Science databases (March 2, 2025). It captured cross-sectional studies that (1) investigated whole-brain activation differences between PUI individuals and healthy controls during inhibitory control tasks, and (2) reported peak coordinates of significant differences. Out of 742 potentially relevant studies, 23 (comprising 548 PUI individuals and 537 healthy controls) were eligible for our analysis. They were subjected to anatomical likelihood estimation (ALE) meta-analysis using extracted coordinates. Results suggest spatial convergence in the left middle frontal gyrus and the right superior parietal lobule. These brain regions mediate executive control and top-down regulation. We conclude that the observed increased activation in the left middle frontal gyrus and right superior parietal lobule during inhibitory control tasks is a neural pattern that is often associated with PUI. Neurotransmitter enrichment analysis revealed that this brain activation pattern in PUI individuals was negatively associated with 5-HTT distribution, implicating a potential involvement of serotonergic systems in inhibitory control alterations. Further investigations are needed to elucidate the nature of brain activation differences across various PUI subtypes, establishing causality, and generalizability to other samples.

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AAMAS Conference 2026 Conference Paper

LLM-based Agents in Supply Chain Games: The Role of Incomplete Information and Model Heterogeneity

Jiuyun Jiang
Yuecheng Hong
Jiangnan Shi
Song Huang
Jin Yang
Guangxin Jiang
Xiaomeng Guo
Guang Xiao

Effective collaboration is essential for mitigating market volatility, yet complete information sharing among partners is often impractical. By employing diverse Large Language Models as autonomous agents, we design controlled experiments in which information is shared only among subsets of enterprises, approximating realistic business environments. Our results reveal a counterintuitive finding: partial information sharing can generate system level benefits comparable to those achieved under full transparency. We further compare agent behavior and identify differences in decision stability. DeepSeek exhibiting the most consistent performance, followed by Qwen and Llama. Finally, experiments within a Llama based environment show that introducing a higher capability model can improve both stability and aggregate performance. Overall, our study provides a scalable experimental framework for artificial society modeling and demonstrates the potential of LLM-based agent simulations for investigating complex socio economic systems.

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ECAI Conference 2025 Conference Paper

MixGAN: A Hybrid Semi-Supervised and Generative Approach for DDoS Detection in Cloud-Integrated IoT Networks

Tongxi Wu
Chenwei Xu
Jin Yang

The proliferation of cloud-integrated IoT systems has intensified exposure to Distributed Denial of Service (DDoS) attacks due to the expanded attack surface, heterogeneous device behaviors, and limited edge protection. However, DDoS detection in this context remains challenging because of complex traffic dynamics, severe class imbalance, and scarce labeled data. While recent methods have explored solutions to address class imbalance, many still struggle to generalize under limited supervision and dynamic traffic conditions. To overcome these challenges, we propose MixGAN, a hybrid detection method that integrates conditional generation, semi-supervised learning, and robust feature extraction. Specifically, to handle complex temporal traffic patterns, we design a 1-D WideResNet backbone composed of temporal convolutional layers with residual connections, which effectively capture local burst patterns in traffic sequences. To alleviate class imbalance and label scarcity, we use a pretrained CTGAN to generate synthetic minority-class (DDoS attack) samples that complement unlabeled data. Furthermore, to mitigate the effect of noisy pseudo-labels, we introduce a MixUp-Average-Sharpen (MAS) strategy that constructs smoothed and sharpened targets by averaging predictions over augmented views and reweighting them towards high-confidence classes. Experiments on NSL-KDD, BoT-IoT, and CICIoT2023 demonstrate that MixGAN achieves up to 2. 5% higher accuracy and 4% improvement in both TPR and TNR compared to state-of-the-art methods, confirming its robustness in large-scale IoT-cloud environments. The source code is publicly available at https: //github. com/0xCavaliers/MixGAN.

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AAAI Conference 2025 Conference Paper

Multimodal Variational Autoencoder: A Barycentric View

Peijie Qiu
Wenhui Zhu
Sayantan Kumar
Xiwen Chen
Jin Yang
Xiaotong Sun
Abolfazl Razi
Yalin Wang

Multiple signal modalities, such as vision and sounds, are naturally present in real-world phenomena. Recently, there has been growing interest in learning generative models, in particular variational autoencoder (VAE), to for multimodal representation learning especially in the case of missing modalities. The primary goal of these models is to learn a modality-invariant and modality-specific representation that characterizes information across multiple modalities. Previous attempts at multimodal VAEs approach this mainly through the lens of experts, aggregating unimodal inference distributions with a product of experts (PoE), a mixture of experts (MoE), or a combination of both. In this paper, we provide an alternative generic and theoretical formulation of multimodal VAE through the lens of barycenter. We first show that PoE and MoE are specific instances of barycenters, derived by minimizing the asymmetric weighted KL divergence to unimodal inference distributions. Our novel formulation extends these two barycenters to a more flexible choice by considering different types of divergences. In particular, we explore the Wasserstein barycenter defined by the 2-Wasserstein distance, which better preserves the geometry of unimodal distributions by capturing both modality-specific and modality-invariant representations compared to KL divergence. Empirical studies on three multimodal benchmarks demonstrated the effectiveness of the proposed method.

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EAAI Journal 2025 Journal Article

Susceptibility risk assessment of oil and gas pipeline geological hazards in mountainous areas based on data-driven model

Yuxiang Yang
Benji Wang
Xiao Cen
Bowen Shao
Baikang Zhu
Jin Yang
Bingyuan Hong

Geological hazards are recognized as causing significant damage to oil and gas pipelines, often resulting in catastrophic loss of life and property and hindering societal progress. In this study, a data-driven evaluation model is developed by integrating the Information Value method (IVM) with a Back Propagation Neural Network (BPNN) to assess the susceptibility of geological hazards in mountainous oil and gas pipelines. The IVM is used to identify non-hazardous areas, optimizing sample selection and reducing training errors, while the BPNN is employed to determine the weights of evaluation indicators, enhancing accuracy. First, an evaluation index system is proposed that comprehensively considers the natural geographical conditions and main disaster types. Next, non-disaster areas are located using the IVM and combined with disaster-prone areas to form the sample data. The sample data is then input into a BPNN for training, and the weights of each evaluation index are obtained from the trained network. Finally, a susceptibility risk assessment model is developed based on the derived weights and information values to accurately evaluate the susceptibility of pipeline geological hazards. A pipeline in China's Zhejiang Province's mountainous region is used as an illustration. Compared to the single IVM model and the single BPNN model, the receiver operator characteristic curve shows that the proposed method achieves significant improvements in the area under the curve by 9. 8 % and 11. 2 %, respectively, indicating a high level of evaluation accuracy. This study provides a reliable approach for assessing geological hazard susceptibility, offering scientific support for pipeline planning and hazard mitigation in oil and gas operations.

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YNIMG Journal 2024 Journal Article

Analgesic effect of dance movement therapy: An fNIRS study

Cheng-Cheng Wu
Jin Yang
Xue-Qiang Wang

OBJECTIVE: This study aims to explores the physiological and psychological mechanisms of exercise-induced hypoalgesia (EIH) by combining the behavioral results with neuroimaging data on changes oxy-hemoglobin (HbO) in prefrontal cortex (PFC). METHODS: A total of 97 healthy participants were recruited and randomly divided into three groups: a single dance movement therapy (DMT) group, a double DMT group, and control group. Evaluation indicators included the pressure pain threshold (PPT) test, the color-word stroop task (CWST) for wearing functional near-infrared spectroscopy (fNIRS), and the self-assessment manikin (SAM). The testing time is before intervention, after intervention, and one hour of sit rest after intervention. RESULTS: 1) Repeated measures ANOVA revealed that, there is a time * group effect on the PPT values of the three groups of participants at three time points. After 30 min of acute dance intervention, an increase in the PPT values of 10 test points occurred in the entire body of the participants in the experimental group with a significant difference than the control group. 2) In terms of fNIRS signals, bilateral DLPFC and left VLPFC channels were significantly activated in the experimental group. 3) DMT significantly awakened participants and brought about pleasant emotions, but cognitive improvement was insignificant. 4) Mediation effect analysis found that the change in HbO concentration in DLPFC may be a mediator in predicting the degree of improvement in pressure pain threshold through dance intervention (total effect β = 0.7140). CONCLUSION: In healthy adults, DMT can produce a diffuse EIH effect on improving pressure pain threshold, emotional experience but only showing an improvement trend in cognitive performance. Dance intervention significantly activates the left ventrolateral and bilateral dorsolateral prefrontal cortex. This study explores the central nervous system mechanism of EIH from a physiological and psychological perspective.

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RLDM Conference 2019 Conference Abstract

Deep Reinforcement Learning for Job Scheduling in Computation Graphs on Heterogeneous Platforms

Adam Stooke
Ignasi Clavera
Wenyuan Li
Xin Zhang
Jin Yang

Job scheduling in heterogeneous computing platforms is a challenging real-world problem, per- vasive across a range of system scales. In this work, we use deep reinforcement learning (RL) to optimize scheduling on a parallel computing platform modeled from a real-world networking device. The goal is to complete, as quickly as possible, the computation of a series of jobs with data inter-dependencies, express- ible as a (directed acyclic) job graph. The controller must plan over long horizons to match computation load balancing against the latency of transferring data across the platform. We explore practical aspects of several design spaces: specfication of the (PO)MDP, including reward function with shaping; neural net- work architecture; and learning algorithm. Challenges to learning include: the high-dimensional input and output spaces, partial observability, a sparse figure of merit (graph completion time), and the long problem horizon–in excess of 10, 000 sequential decisions for a realistic job graph. On a high-fidelity simulator, we dramatically outperform two heuristic scheduling schemes while using little-to-no prior domain knowledge. Finally, we discuss future research opportunities in this rich problem, to include reward design, learning algorithm, choices in MDP state specification (e. g. device readout, graph look-ahead), and application of graph-nets. To our knowledge, this work is a unique application of deep RL to a realistic, industrial job scheduling problem, and we believe it has the potential to impact a broad class of computing technologies.

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IS Journal 2014 Journal Article

An Energy-Efficient and Swarm Intelligence-Based Routing Protocol for Next-Generation Sensor Networks

Yong Wang
Changle Li
Yulong Duan
Jin Yang
Xiang Cheng

After providing a brief overview of routing protocols for next-generation sensor networks (NGSNs), the authors propose Bee-Sensor-C, an energy-efficient, swarm intelligence-based, and scalable multipath routing protocol that integrates dynamic clustering, multipath routing, and bee-inspired routing to meet the performance requirements of NGSNs. A performance evaluation is also provided.

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