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arXiv 2025年

作者： Chen, Haipeng Wu, Sifan Wang, Zhigang Yin, Yifang Jiao, Yingying Lyu, Yingda Liu, Zhenguang College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China College of Computer Science and Technology Zhejiang Gongshang University China A*STAR Singapore Public Computer Education and Research Center Jilin University China The State Key Laboratory of Blockchain and Data Security Zhejiang University China Institute of Blockchain and Data Security China

Video-based human pose estimation has long been a fundamental yet challenging problem in computer vision. Previous studies focus on spatio-temporal modeling through the enhancement of architecture design and optimization strategies. However, they overlook the causal relationships in the joints, leading to models that may be overly tailored and thus estimate poorly to challenging scenes. Therefore, adequate causal reasoning capability, coupled with good interpretability of model, are both indispensable and prerequisite for achieving reliable results. In this paper, we pioneer a causal perspective on pose estimation and introduce a causal-inspired multitask learning framework, consisting of two stages. In the first stage, we try to endow the model with causal spatio-temporal modeling ability by introducing two self-supervision auxiliary tasks. Specifically, these auxiliary tasks enable the network to infer challenging keypoints based on observed keypoint information, thereby imbuing causal reasoning capabilities into the model and making it robust to challenging scenes. In the second stage, we argue that not all feature tokens contribute equally to pose estimation. Prioritizing causal (keypoint-relevant) tokens is crucial to achieve reliable results, which could improve the interpretability of the model. To this end, we propose a Token Causal Importance Selection module to identify the causal tokens and non-causal tokens (e.g., background and objects). Additionally, non-causal tokens could provide potentially beneficial cues but may be redundant. We further introduce a non-causal tokens clustering module to merge the similar non-causal tokens. Extensive experiments show that our method outperforms state-of-the-art methods on three large-scale benchmark datasets. Copyright © 2025, The Authors. All rights reserved.

关键词： Self-supervised learning

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Low-altitude Friendly-Jamming for Satellite-Maritime Communications via Generative AI-enabled Deep Reinforcement Learning

arXiv

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arXiv 2025年

作者： Huang, Jiawei Wang, Aimin Sun, Geng Li, Jiahui Wang, Jiacheng Niyato, Dusit Leung, Victor C.M. College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518115 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering The University of British Columbia VancouverV6T 1Z4 Canada

Low Earth Orbit (LEO) satellites can be used to assist maritime wireless communications for data transmission across wide-ranging areas. However, extensive coverage of LEO satellites, combined with openness of channels, can cause the communication process to suffer from security risks. This paper presents a low-altitude friendly-jamming LEO satellite-maritime communication system enabled by a unmanned aerial vehicle (UAV) to ensure data security at the physical layer. Since such a system requires trade-off policies that balance the secrecy rate and energy consumption of the UAV to meet evolving scenario demands, we formulate a secure satellite-maritime communication multi-objective optimization problem (SSMCMOP). In order to solve the dynamic and long-term optimization problem, we reformulate it into a Markov decision process. We then propose a transformer-enhanced soft actor critic (TransSAC) algorithm, which is a generative artificial intelligence-enable deep reinforcement learning approach to solve the reformulated problem, so that capturing global dependencies and diversely exploring weights. Simulation results demonstrate that the TransSAC outperforms various baselines, and achieves an optimal secrecy rate while effectively minimizing the energy consumption of the UAV. Moreover, the results find more suitable constraint values for the system. Copyright © 2025, The Authors. All rights reserved.

关键词： Multiobjective optimization

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A Description Model of Multi-Step Attack Planning Domain Based on knowledge Representation

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Chinese Journal of Electronics 2023年第3期22卷 437-441页

作者： HU Liang XIE Nannan CHAI Sheng Nurbol Key Laboratory of Symbolic Computing and Knowledge Engineering of Ministry of Education Computer Science and Technology College Jilin University Changchun China Information Science and Engineering College Xinjiang University Urumqi China

Alerts of intrusion detection system are numerous, complex, and difficult to analyze. Alert correlation of multi-step attack is one of the solutions to this problem. Intelligence planning is an important research area of artificial intelligence, and always used in fields problems. Intelligence planning is used to deal with multi-step attack recognition in this work. A multi-step attack planning domain description model is proposed, in order to describe the attack knowledge base, and based on knowledge representation. The model is described with PDDL (Planning domain definition language). Experiments with DARPA 2000 dataset showed the model proposed can recognize multi-step attacks effectively, and is available and practical.

关键词： Correlation knowledge based systems Intrusion detection knowledge representation Medical services Planning Network intrusion

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Short Tail:taming tail latency for erasure-code-based in-memory systems

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Frontiers of Information Technology & Electronic engineering 2022年第11期23卷 1646-1657页

作者： Yun TENG Zhiyue LI Jing HUANG Guangyan ZHANG College of Computer Science and Technology Jilin UniversityChangchun 130012China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchun 130012China Beijing National Research Center for Information Science and Technology(Tsinghua University) Beijing 100084China

In-memory systems with erasure coding(EC)enabled are widely used to achieve high performance and data ***,as the scale of clusters grows,the server-level fail-slow problem is becoming increasingly frequent,which can create long tail *** influence of long tail latency is further amplified in EC-based systems due to the synchronous nature of multiple EC *** this paper,we propose an EC-enabled in-memory storage system called ShortTail,which can achieve consistent performance and low latency for both reads and ***,ShortTail uses a lightweight request monitor to track the performance of each memory node and identify any fail-slow ***,ShortTail selectively performs degraded reads and redirected writes to avoid accessing fail-slow ***,ShortTail posts an adaptive write strategy to reduce write amplification of small *** implement ShortTail on top of Memcached and compare it with two baseline *** experimental results show that ShortTail can reduce the P99 tail latency by up to 63.77%;it also brings significant improvements in the median latency and average latency.

关键词： Erasure code In-memory system Node fail-slow Small write Tail latency

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2.5D ASF-UNet: Adjacent Slice Spatial Feature Fusion Model for WMH Segmentation from 3D MR Brain Image

2.5D ASF-UNet: Adjacent Slice Spatial Feature Fusion Model f...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Lan Huang Yinglu Sun Ziqi Zhao Chunjie Guo Yan Wang Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun China Department of Radiology the First Hospital of Jilin University Changchun China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Segmenting brain white matter hyperintensities (WMH) from 3D Magnetic Resonance (MR) images is crucial for the diagnosis, treatment, and prognosis of Multiple Sclerosis (MS). Unlike common 2D images, this task is more challenging and time-consuming. Classical deep learning methods for 3D image segmentation face two main challenges: 1) Pure 3D networks have more parameters and are prone to overfitting. 2) When using 2D networks to segment slices of 3D images, the lack of 3D structural information results in suboptimal segmentation after reconstruction. To address these difficulties, we propose the 2.5D ASF-UNet, which employs the 2.5D workflow and uses adjacent slices as the input for 2D segmentation network ASF-UNet. In ASF-UNet, separate down-sampling paths are used for the adjacent slices, and the Local Spatial Attention Module (LSAM) is designed to more effectively integrate 3D spatial information into the 2D network. Additionally, the Conv_Spectral_Block (CSB) is designed to extract and integrate local and global features. It allows the model to capture global spatial structures while preserving detailed information. Experimental results on MICCAI MSSEG 2016 and Local MS datasets show that 2.5D ASF-UNet achieve better segmentation performance than other deep learning methods.

关键词： Deep learning Image segmentation Solid modeling Three-dimensional displays Multiple sclerosis Feature extraction Brain modeling White matter Prognostics and health management Overfitting

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Shortcut Learning in In-Context Learning: A Survey

arXiv

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arXiv 2024年

作者： Song, Rui Li, Yingji Shi, Lida Giunchiglia, Fausto Xu, Hao College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University China Department of Information Engineering and Computer Science University of Trento Italy

Shortcut learning refers to the phenomenon where models employ simple, non-robust decision rules in practical tasks, which hinders their generalization and robustness. With the rapid development of Large Language Models (LLMs) in recent years, an increasing number of studies have shown the impact of shortcut learning on LLMs. This paper provides a novel perspective to review relevant research on shortcut learning in In-Context Learning (ICL). It conducts a detailed exploration of the types of shortcuts in ICL tasks, their causes, available benchmarks, and strategies for mitigating shortcuts. Based on the corresponding observations, we summarize the unresolved issues in existing research and attempts to outline the future research landscape of shortcut learning. © 2024, CC BY.

关键词： Contrastive Learning

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PLPR: Towards Efficient and Privacy-Preserving LBSNs-Based POI Recommendation in Cloud

PLPR: Towards Efficient and Privacy-Preserving LBSNs-Based P...

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IEEE International Conference on Communications (ICC)

作者： Lili Sun Yonggang Zhang Yandong Zheng Rongxing Lu Hui Zhu Key Laboratory of Symbolic Computation and Knowledge Engineer Jilin University Changchun China State Key Laboratory of Integrated Services Networks Xidian University Xi'an China Facuity of Computer Science University of New Brunswick Fredericton Canada

With the popularity of location-based social networks (LBSNs), locations and social relationships have been considered to be important factors in point-of-interest (POI) recommendation services. The boom of cloud computing has driven data owners to outsource the LBSN data and the POI recommendation services to the cloud with powerful computing and storage capabilities. However, as the data usually contains sensitive information, it should be encrypted before being out-sourced, and consequently, the POI recommendation has to be processed over encrypted data. Although several privacy-preserving LBSNs-based POI recommendation schemes have been proposed, they are either inapplicable to the outsourcing scenario or have issues with the recommendation accuracy. Aiming at addressing these issues, in this paper, we propose an efficient and privacy-preserving LBSNs-based POI recommendation scheme (PLPR). Specifically, we first index the users' social relationships with Bloom filters and then organize the user resident location and social relationship dataset into a Vantage Point (VP) tree. Then, we design an efficient LSBNs-based POI recommendation algorithm based on the VP tree. After that, we design a privacy-preserving range determination protocol (PRD) and a privacy-preserving neighbor determination protocol (PND) to respectively protect the privacy of locations and social relationships in the designed algorithm and propose our PLPR scheme. Security analysis shows that our scheme is privacy-preserving, and performance evaluation demonstrates that our scheme is also efficient.

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HVIS: A Human-like Vision and Inference System for Human Motion Prediction

arXiv

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arXiv 2025年

作者： Lyu, Kedi Chen, Haipeng Liu, Zhenguang Yin, Yifang Lin, Yukang Jiao, Yingying College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Institute of Blockchain and Data Security Hangzhou China A*STAR Singapore Tsinghua Shenzhen International Graduate School Nanshan District Shenzhen China

Grasping the intricacies of human motion, which involve perceiving spatio-temporal dependence and multi-scale effects, is essential for predicting human motion. While humans inherently possess the requisite skills to navigate this issue, it proves to be markedly more challenging for machines to emulate. To bridge the gap, we propose the Human-like Vision and Inference System (HVIS) for human motion prediction, which is designed to emulate human observation and forecast future movements. HVIS comprises two components: the human-like vision encode (HVE) module and the human-like motion inference (HMI) module. The HVE module mimics and refines the human visual process, incorporating a retina-analog component that captures spatiotemporal information separately to avoid unnecessary crosstalk. Additionally, a visual cortex-analogy component is designed to hierarchically extract and treat complex motion features, focusing on both global and local features of human poses. The HMI is employed to simulate the multi-stage learning model of the human brain. The spontaneous learning network simulates the neuronal fracture generation process for the adversarial generation of future motions. Subsequently, the deliberate learning network is optimized for hard-to-train joints to prevent misleading learning. Experimental results demonstrate that our method achieves new state-of-the-art performance, significantly outperforming existing methods by 19.8% on Human3.6M, 15.7% on CMU Mocap, and 11.1% on G3D. © 2025, CC BY.

关键词： Motion estimation

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Self-Ensembling Semi-Supervised Model for Bone X-ray Images Landmark Detection

Self-Ensembling Semi-Supervised Model for Bone X-ray Images ...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Bai, Tian Liu, Shenyao Wang, Yuzhao Wang, Yu Dong, Dong Jilin University College of Computer Science and Technology Changchun China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun China College of Software Jilin University Changchun China Department of Radiology First Hospital of Jilin University Changchun China

ISBN: (纸本)9781665401265

Bone image landmark detection plays a vital role in orthopedic diseases diagnosis and analysis. As a promising approach, the data-driven convolutional neural network has been widely applied in landmarks detection. However, the model's performance relies heavily on numerous high-quality labeled data, which are difficult to access. A practical solution for this issue in bone images landmark detection has not been provided, so we propose a framework of semi-supervised bone images landmark detection encouraged by the recent success of the self-ensembling method. The student and teacher networks share the same structure in our framework, where unlabeled data are utilized by enforcing the prediction consistency of two networks under different perturbations to enhance the network's performance. The two networks use a mutual learning approach to improve the efficiency of using unlabeled data. In addition, we also propose a transformative perturbation scheme to prevent model overfitting during training and enhance the generalization capability of the model on unseen data. Experimental results on an in-house hip dataset collected from the actual process of clinical diagnosis and a publicly available dataset demonstrate that our framework has excellent performance compared with other semi-supervised methods. © 2021 IEEE.

关键词： Convolutional neural networks

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An Enhanced Approach for Few-Shot Segmentation Via Smooth Downsampling Mask and Label Smoothing Loss

SSRN

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SSRN 2023年

作者： Jin, Hailong Li, Huiying College of Computer Science and Technology Jilin Province Changchun China Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Changchun130012 China

Few-shot semantic segmentation aims to segment new categories with only a small number of annotated images. Previous methods mainly focused on exploiting the pixel-level correlation between the support image and the query image, combined with attention-based methods, resulting in significant advancements. In this paper, we introduce a new perspective to enhance few-shot segmentation. We identify that utilizing the bilinear interpolation method to downsample the mask leads to the loss of fine-grained information from the target features. To address this issue, we propose a Smooth Downsampling Mask (SDM) method. The SDM method is designed to retain more effective target semantic features by employing a cascaded downsampling approach with a smooth kernel for mask processing. Additionally, we propose a label smoothing loss to further enhance the performance, which provides direct guidance for low-resolution feature map optimization. Notably, our proposed method involves no additional learnable parameters and introduces only a small amount of extra computation. To validate the effectiveness of our approach, we conduct extensive experiments on three public benchmarks, PASCAL-$5^i$, COCO-$20^i$, and FSS-$1000$, and achieve considerable improvements. © 2023, The Authors. All rights reserved.

关键词： Semantics

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