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An Area Optimization Approach for Large-Scale RM-TB Dual Logic Circuits Based on a Multitasking Optimization Algorithm

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IEEE Transactions on Computers 2025年

作者： Wu, Xiaoqian Wang, Peng Li, Shaoquan Liu, Huaxiao Liu, Lei Jilin University College of Computer Science and Technology Changchun China Key Laboratory of Symbolic Computation China Knowledge Engineering of Ministry of Education China

Logic synthesis is a crucial step in integrated circuit design, and area optimization is an indispensable part of this process. However, the area optimization problem for large-scale Fixed Polarity Reed-Muller (FPRM) circuits is an NP-hard problem. To address this problem, we divide Boolean circuits into small-scale circuits based on the idea of divide-and-conquer using the proposed grouping decomposition mechanism. Each small-scale Boolean circuit is transformed into an FPRM circuit by a polarity transformation algorithm. To ensure the circuit’s functionality remains unaffected, we integrate FPRM circuits into an FPRM and Boolean (RM-TB) dual logic circuit based on the proposed gate-level integration. However, the area optimization problem of RM-TB dual logic circuits is a multi-task, high-dimensional, and multi-extremal combinatorial optimization problem. Therefore, we propose a Multipopulation Multitasking Optimization Algorithm (MMuOA) that integrates self-evolution with a multitasking equilibrium optimizer and cross-task evolution through knowledge sharing and transfer. This forms a dynamic optimization framework for simultaneously searching for the optimal polarity corresponding to the minimal area of RM-TB dual logic circuits. Moreover, we propose an Area Optimization Approach (AOA) for an RM-TB dual logic circuit with the minimum area using the MMuOA. Experimental results based on the Microelectronics Center of North Carolina (MCNC) Benchmark test circuits demonstrate the effectiveness and superiority of the AOA compared to the state-of-the-art area optimization approach. © 1968-2012 IEEE.

关键词： Combinatorial optimization

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Enhancing Human Pose Estimation in the Internet of Things via Diffusion Generative Models

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IEEE Internet of Things Journal 2025年第10期12卷 13556-13567页

作者： Wu, Sifan Zhang, Hongzhe Liu, Zhenguang Chen, Haipeng Jiao, Yingying Jilin University College of Computer Science and Technology the Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun130012 China School of discipline inspection and supervision China University of Political Science and Law Beijing100088 China Zhejiang University State Key Laboratory of Blockchain and Data Security Hangzhou310027 China Institute of Blockchain and Data Security Hangzhou310059 China Zhejiang University of Technology College of Computer Science and Technology Hangzhou310014 China

With the ongoing development of public video surveillance technology, accurate human pose estimation is becoming increasingly important in urban administration and law enforcement. However, existing methods rely on large-scale dense annotations, which are labor-intensive and time-consuming. To tackle this, we propose SparsePose which leverages training videos with sparse annotations (labeled every k frames) to learn to propagate temporal poses that help to estimate the poses in unlabeled frames. Technically, we engage in a novel dual-branch architecture that combines 1) pose forecasting of the consecutive neighboring frames with 2) visual clues of the current frame and the nearest labeled frames. We theoretically derive the intra-branch and inter-branch mutual information loss to supervise that maximized pose-relevant features are extracted from the current frame and different branches complement each other to approach precise pose estimation. Additionally, we propose a diffusion generative enhancement, which improves the robustness of the model to challenging scenes from the perspective of diversity. Empirical results show that our method significantly outperforms the state-of-the-art methods in sparsely labeled pose estimation on three benchmark datasets. © 2025 IEEE.

关键词： Generative adversarial networks

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Auxiliary Tasks Benefit Skeleton-based Action Recognition

Auxiliary Tasks Benefit Skeleton-based Action Recognition

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yuheng Yang Haipeng Chen College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Skeleton-based action recognition has long been a fundamental and intriguing problem in machine intelligence. This task is challenging due to pose occlusion and rapid motion, which typically results in incomplete or noisy skeleton data. State-of-the-art methods tend to learn human motion directly from these corrupted skeletons as if they were reliable. Unfortunately, this might lead to unsatisfactory results when key regions of the skeleton are occluded or disturbed. To tackle the problem, we propose a novel framework that integrates auxiliary tasks into a motion modeling network. These auxiliary tasks corrupt partial human skeletons with masking or noise and then force the network to recover the corrupted data, explicitly facilitating robust feature representation learning. We further propose supervising the auxiliary tasks with mutual information losses, mathematically ensuring feature consistency and spatial alignment between the recovered and original skeleton data. Empirically, our approach sets the new state-of-the-art performance on three benchmark datasets.

关键词： Representation learning Noise Speech recognition Benchmark testing Skeleton Mathematical models Reliability Noise measurement Speech processing Mutual information

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Structure-Based Uncertainty Estimation for Source-Free Active Domain Adaptation

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IET Computer Vision 2025年第1期19卷

作者： Ouyang, Jihong Zhang, Zhengjie Meng, Qingyi Chi, Jinjin 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

Active domain adaptation (active DA) provides an effective solution by selectively labelling a limited number of target samples to significantly enhance adaptation performance. However, existing active DA methods often struggle in real-world scenarios where, due to data privacy concerns, only a pre-trained source model is available, rather than the source samples. To address this issue, we propose a novel method called the structure-based uncertainty estimation model (SUEM) for source-free active domain adaptation (SFADA). To be specific, we introduce an innovative active sample selection strategy that combines both uncertainty and diversity sampling to identify the most informative samples. We assess the uncertainty in target samples using structure-wise probabilities and implement a diversity selection method to minimise redundancy. For the selected samples, we not only apply standard-supervised loss but also conduct interpolation consistency training to further explore the structural information of the target domain. Extensive experiments across four widely used datasets demonstrate that our method is comparable to or outperforms current UDA and active DA methods. © 2025 The Author(s). IET Computer Vision published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： computer vision convolutional neural nets image classification

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A Simple Graph Contrastive Learning Framework for Short Text Classification

arXiv

引用

arXiv 2025年

作者： Liu, Yonghao Giunchiglia, Fausto Huang, Lan Li, Ximing Feng, Xiaoyue Guan, Renchu Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University China University of Trento Italy

Short text classification has gained significant attention in the information age due to its prevalence and real-world applications. Recent advancements in graph learning combined with contrastive learning have shown promising results in addressing the challenges of semantic sparsity and limited labeled data in short text classification. However, existing models have certain limitations. They rely on explicit data augmentation techniques to generate contrastive views, resulting in semantic corruption and noise. Additionally, these models only focus on learning the intrinsic consistency between the generated views, neglecting valuable discriminative information from other potential views. To address these issues, we propose a Simple graph contrastive learning framework for Short Text Classification (SimSTC). Our approach involves performing graph learning on multiple text-related component graphs to obtain multi-view text embeddings. Subsequently, we directly apply contrastive learning on these embeddings. Notably, our method eliminates the need for data augmentation operations to generate contrastive views while still leveraging the benefits of multi-view contrastive learning. Despite its simplicity, our model achieves outstanding performance, surpassing large language models on various datasets. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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Intrusion detection system for autonomous vehicles using sensor spatio-temporal information

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Computers and Security 2025年 156卷

作者： Liu, Qingxin Qin, Guihe Liang, Yanhua Song, Jiaru Liu, Wanning Zhou, Xue 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

Connected Autonomous Vehicles (CAVs) have great potential to improve driving safety and comfort, but they still face cybersecurity risks. Intrusion Detection Systems (IDS) have now become the primary means of addressing this problem. There are two weaknesses in existing studies that consider sensor correlation. First, few studies focus on the degree of correlation between sensors. Second, existing studies usually focus only on anomaly detection and ignore the precise location of attack targets. In this paper, we propose a two-stage intrusion detection system based on in-vehicle sensors spatio-temporal information. The first stage is set as a behavior predictor, which uses historical data to predict current data. Where the spatial Multi-head Graph Attention (MGAT) layer considers the degree of correlation among sensors through the attention weights in the graph structure, and the temporal Multi-head Graph Attention layer models the dependence of data at different time points of a single sensor. In the second phase, the attack detector first detects anomalies based on the deviation between predicted and observed values, after which a threshold deviation ratio is introduced to locate the attacked sensor. Experimental results in real vehicle data sets show that the proposed system can efficiently detect multiple types of attacks with an average F1 score of 98.28%, which is at least 1.45% higher than the existing methods. In various single-sensor attack scenarios, the accuracy of identifying attack targets exceeds 97.00%. © 2025 Elsevier Ltd

关键词： Network intrusion

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Enhancing diabetes complications prediction through knowledge graphs and convolutional networks

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engineering Applications of Artificial Intelligence 2025年 153卷

作者： Cheng, Haitao Zheng, Qunli Li, Peng Xu, He School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China

Early prediction of diabetes complications is crucial for timely intervention and effective disease management. However, current deep learning approaches often lack sufficient representation of diabetes knowledge and rely on large-scale annotated data. To address this, we propose a knowledge Graphs-enhanced Diabetes Complications Prediction (KGDCP) model, which integrates a physical examination knowledge graph and an improved Diabetes knowledge Graph (DiaKG) using self-attention-based convolutional neural networks. We construct the physical examination knowledge graph based on normal reference ranges of examination indicators and enhance DiaKG through knowledge deduplication and coreference resolution, improving its accuracy and comprehensiveness. Furthermore, we employ the embeddings of these two knowledge graphs to represent examination data and diagnostic information. A token-to-token (token2token) self-attention is utilized to explore dependencies among examination indicators, and convolutional neural networks extract local features to generate representation vectors. Additionally, source-to-token (source2token) self-attention assesses dependencies between diagnostic entities and the entire entity set, with entity-to-Physical Examination (entity2PE) attention gauging entity relevance to examination data representation vectors. Our model combines these features to enhance diabetes complications prediction, outperforming traditional models in predicting cerebral infarction and peripheral neuropathy. © 2025

关键词： knowledge graph

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Enabling Generalized Zero-Shot Vulnerability Classification

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Hu, Jinghao Guo, Jinsong Luo, Chen Hu, Yang Lanzinger, Matthias Li, Zhanshan Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education College of Software Changchun130012 China University College London Department of Computer Science United Kingdom Amazon United States United Kingdom University of Oxford Department of Computer Science United Kingdom Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education College of Computer Science and Technology Changchun130012 China

Regarding computer security, the growth of code vulnerability types presents a persistent challenge. These vulnerabilities, which may cause severe consequences, necessitate precise classification for effective mitigation. However, the rapid emergence of new vulnerability types complicates the classification process. Traditional methodologies, which often involve human expertise and the manual labeling or generation of example instances, are not only resource-intensive but also struggle to adapt to the dynamic nature of these vulnerabilities. This paper introduces VulnSense, an innovative method that harnesses the capabilities of Generalized Zero-Shot Learning (GZSL) to address the vulnerability classification problem. VulnSense learns about unseen vulnerability classes from the descriptions of these unseen classes, while not requiring to see any instances of these unseen classes. Specifically, VulnSense learns from three main resources: (1) seen classes with labeled code instances;(2) descriptions of these seen classes;and (3) descriptions of "unseen"classes which have no labeled instances. Our experiments underscore VulnSense's superiority over existing GZSL methods in classifying instances of unseen classes. Concurrently, it maintains a performance parity with traditional labeled-example based learning methods in classifying instances of seen vulnerabilities. VulnSense demonstrates the potential of using GZSL for vulnerability classification, while also highlighting challenges that inspire future work. © 2004-2012 IEEE.

关键词： Zero-shot learning

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Fairness-Aware Budgeted Edge Server Placement for Connected Autonomous Vehicles

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IEEE Transactions on Mobile Computing 2025年第6期24卷 4762-4776页

作者： Wu, Jintao Xu, Xiaolong Cui, Guangming Zhang, Yiwen Qi, Lianyong Dou, Wanchun Cai, Zhipeng Nanjing University of Information Science & Technology Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services School of Software Nanjing210044 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China Nanjing University of Information Science & Technology Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services School of Software China Nanjing University State Key Laboratory for Novel Software Technology China Anhui University School of Computer Science and Technology Anhui230031 China College of Computer Science and Technology Qingdao266580 China Nanjing University State Key Laboratory for Novel Software Technology Nanjing210023 China Georgia State University Department of Computer Science United States

Mobile edge computing (MEC) considerably enhances the capabilities and performance of connected autonomous vehicles (CAVs) by deploying edge servers (ESs) on roadside units (RSUs) near CAVs, thereby ensuring low-latency services. Given the constrained and costly nature of ES resources (computing, storage, and bandwidth), equitable ES utilization is critical for CAV operations. However, fairness considerations are often overlooked in current budgeted edge server placement (ESP) strategies, potentially worsening resource imbalances and compromising user experience. This paper investigates the fairness-aware budgeted edge server placement (FESP) problem within RSUs, proving its NP-hardness. To address FESP, we first propose FESP-O, an integer programming-based optimal approach for small-scale problems, followed by FESP-APX, an approximation approach for large-scale scenarios that provides near-optimal solutions. We analyze the time complexity and approximation ratio of our proposed algorithms and validate their efficacy through experiments on real-world datasets. Extensive experimental results demonstrate significant performance improvements over baseline and state-of-the-art methods, indicating practical suitability and efficiency. © 2002-2012 IEEE.

关键词： Integer linear programming

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Efficient Sharing of Energy Consumption Data: A Privacy-Preserving Threshold Aggregation Approach

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IEEE Internet of Things Journal 2025年

作者： Li, Guohao Zhou, Lu Lian, Jiale Liu, Siyi Yang, Li Zhong, Yantao Li, Qiang Xidian University School of Computer Science and Technology Xi’an China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun China Co. Ltd Guangdong Shenzhen China

Energy consumption data collected by smart meters is increasingly used by various subscribers in the smart grid for load management, energy monitoring, and policy planning. To protect user privacy, edge-assisted privacy-preserving data aggregation (PPDA) techniques are commonly employed. However, existing methods face several challenges: i) limited scalability, ii) strict trust requirements, and iii) the risk of revealing unique consumption patterns to data collectors. To address these challenges, we propose a privacy-preserving threshold aggregation method that is easily scalable and facilitates efficient energy data sharing under limited trust assumptions. Specifically, we design VFPNTRU, a quantum-resistant homomorphic proxy re-encryption scheme with fault tolerance and re-encryption verification. In VFP-NTRU, smart meters can encrypt data with a public key without the need for prior negotiation of decryption keys with multiple subscribers. Additionally, we develop a privacy threshold collection protocol that uses a verifiable oblivious pseudorandom function to provide privacy guarantees similar to k-anonymity for SM data collection. We further introduce an energy consumption model to determine optimal collection strategies, improving system responsiveness. We provide correctness analysis and prove the security of our scheme. Experimental results demonstrate that our approach outperforms existing PPDA methods, making it particularly suitable for resource-constrained smart meters and central servers managing large-scale energy data. © 2014 IEEE.

关键词： Differential privacy

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