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5th International Seminar on Artificial intelligence, Networking and Information Technology, AINIT 2024

作者： Yao, Yahu Lu, Rong Wu, Jun Tao, Zaiyang Qu, Lei Key Laboratory of Computational Intelligence and Signal Processing Ministry of Education Hefei China Hefei China

ISBN: (纸本)9798350385557

We can focus on the challenges posed by traditional registration algorithms and the limitations of U-Net-based architectures in capturing sufficient semantic information due to their shallow coding layers with small receptive fields. We want to explore how newer architectures or modifications to existing ones could address these limitations. Inspired by introduced deformable convolutional networks, we plan to fuse deformable convolution into our proposed network structure. Building on its success in 2D target detection, we apply deformable convolution to 3D image registration networks. The Deform U-Net proposed in this paper can compensate for the shortcomings of existing registration networks by obtaining more semantic information with unsupervised training without Introduction of additional features. We conducted evaluations on two publicly available human brain MRI, specifically LPBA40 and IBSR18. On the LPBA40 and ISBR18 datasets, the DSC reached 68.82, 62.30, respectively. The experimental results show that the Deform U-N et image registration method outperforms the VoxelMorph image registration method. © 2024 IEEE.

关键词： Convolution

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Two-Hop Partial Task Offloading and Resource Allocation in Air-Ground Integrated Mobile Edge Computing Network: A DRL-based Method

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IEEE Internet of Things Journal 2025年第12期12卷 21443-21456页

作者： Li, Shichao Lu, Bingji Ale, Laha Chen, Hongbin Tan, Fangqing Huang, Jingyue Guilin University of Electronic Technology Guangxi Key Laboratory of Wireless Broadband Communication and Signal Processing Guilin541004 China Southwest Jiaotong University School of Computing and Artificial Intelligence Chengdu610031 China

The integration of mobile edge computing (MEC) and air-ground integrated network is viewed as a crucial technology for Internet of remote things (IoRT) devices. It provides widespread service coverage and allows the tasks of IoRT devices to be executed by the unmanned aerial vehicles (UAVs) and the high altitude platforms (HAPs). In this paper, we investigate a joint partial task offloading, resource allocation, and UAV trajectory design problem to minimize the total task offloading delay of all IoRT devices in the air-ground integrated MEC network. Given that the problem is non-convex and hard to solve by the traditional methods, we convert it into a Markov decision process (MDP) and leverage the deep reinforcement learning method to address it. Considering the complexity of the MDP grows with the number of the IoRT devices and the UAVs increasing, the primal problem is decomposed into two subproblems: the UAV trajectory design and IoRT device power control subproblem, and the partial task offloading and resource allocation subproblem. To address these two subproblems, we apply the basic concepts of the multi-agent deep deterministic policy gradient (MADDPG) and the independent proximal policy optimization (IPPO) methods, respectively. Additionally, we introduce the enhanced prioritized experience replay and noise value to improve both the convergence performance and rate. This leads to the development of the MADDPG-improved prioritized experience replay (MADDPG-IPER) algorithm and noise value-IPPO (NV-IPPO) algorithm. Based on the solution of these two subproblems, a joint partial task offloading, resource allocation, and UAV trajectory design (JPTORAUTD) algorithm is proposed. Simulation results present that the proposed JPTORAUTD algorithm outperforms other benchmark algorithms in terms of reducing the total task offloading delay. © 2025 IEEE.

关键词： Resource allocation

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DMFVAE:miRNA-disease associations prediction based on deep matrix factorization method with variational autoencoder

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Frontiers of Computer Science 2024年第6期18卷 259-270页

作者： Pijing WEI Qianqian WANG Zhen GAO Ruifen CAO Chunhou ZHENG Information Materials and Intelligent Sensing Laboratory of Anhui Province Institutes of Physical Science and Information TechnologyAnhui UniversityHefei 230601China Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui UniversityHefei 230601China Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial IntelligenceAnhui UniversityHefei 230601China

MicroRNAs(miRNAs)are closely related to numerous complex human diseases,therefore,exploring miRNA-disease associations(MDAs)can help people gain a better understanding of complex disease *** increasing number of computational methods have been developed to predict ***,the sparsity of the MDAs may hinder the performance of many *** addition,many methods fail to capture the nonlinear relationships of miRNA-disease network and inadequately leverage the features of network and neighbor *** this study,we propose a deep matrix factorization model with variational autoencoder(DMFVAE)to predict potential *** first decomposes the original association matrix and the enhanced association matrix,in which the enhanced association matrix is enhanced by self-adjusting the nearest neighbor method,to obtain sparse vectors and dense vectors,***,the variational encoder is employed to obtain the nonlinear latent vectors of miRNA and disease for the sparse vectors,and meanwhile,node2vec is used to obtain the network structure embedding vectors of miRNA and disease for the dense ***,sample features are acquired by combining the latent vectors and network structure embedding vectors,and the final prediction is implemented by convolutional neural network with channel *** evaluate the performance of DMFVAE,we conduct five-fold cross validation on the HMDD v2.0 and HMDD v3.2 datasets and the results show that DMFVAE performs ***,case studies on lung neoplasms,colon neoplasms,and esophageal neoplasms confirm the ability of DMFVAE in identifying potential miRNAs for human diseases.

关键词： miRNA-disease association deep matrix factorization self-adjusted nearest neighbor variational encoder network structure

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Fast construction and exploration of performance-cost design space for belief propagation polar decoders

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Science China(Information Sciences) 2025年第3期68卷 401-402页

作者： Chao JI You YOU Weikang QIAN Yongming HUANG Chuan ZHANG Laboratory of Efficient Architectures for Digital-communication and Signal-processing (LEADS) National Mobile Communications Research Laboratory Southeast University Purple Mountain Laboratories University of Michigan-Shanghai Jiao Tong University Joint Institute Ministry of Education Key Laboratory of Artificial Intelligence Shanghai Jiao Tong University

Considering polar codes in the channel coding field, belief propagation(BP) polar decoding stands out as a potential solution for high-throughput applications due to its inherent advantage on high parallelism, and also emerging other improved BP-based decoding such as BP flip(BPF) [1, 2] and BP list(BPL) [3,4] polar decoders.

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A Privacy Preservation Method for Attributed Social Network Based on Negative Representation of Information

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Computer Modeling in Engineering & Sciences 2024年第7期140卷 1045-1075页

作者： Hao Jiang Yuerong Liao Dongdong Zhao Wenjian Luo Xingyi Zhang Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui UniversityHefei230601China Chongqing Research Institute School of Computer Science and Artificial IntelligenceWuhan University of TechnologyWuhan430070China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and TechnologyHarbin Institute of TechnologyShenzhen518055China

Due to the presence of a large amount of personal sensitive information in social networks,privacy preservation issues in social networks have attracted the attention of many *** by the self-nonself discrimination paradigmin the biological immune system,the negative representation of information indicates features such as simplicity and efficiency,which is very suitable for preserving social network ***,we suggest a method to preserve the topology privacy and node attribute privacy of attribute social networks,called ***,a negative survey-based method is developed to disturb the relationship between nodes in the social network so that the topology structure can be kept ***,a negative database-based method is proposed to hide node attributes,so that the privacy of node attributes can be preserved while supporting the similarity estimation between different node attributes,which is crucial to the analysis of social *** evaluate the performance of the AttNetNRI,empirical studies have been conducted on various attribute social networks and compared with several state-of-the-art methods tailored to preserve the privacy of social *** experimental results show the superiority of the developed method in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topology disturbing and attribute hiding *** experimental results show the superiority of the developed methods in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topological interference and attribute-hiding components.

关键词： Attributed social network topology privacy node attribute privacy negative representation of information negative survey negative database

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Linear Subspace Surrogate Modeling for Large-Scale Expensive Single/Multi-Objective Optimization

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IEEE Transactions on Evolutionary computation 2023年第3期29卷 1-1页

作者： Si, Langchun Zhang, Xingyi Tian, Ye Yang, Shangshang Zhang, Limiao Jin, Yaochu School of Computer Science and Technology Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei China School of Artificial Intelligence Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Hefei China Faculty of Technology Bielefeld University Bielefeld Germany

Despite that the surrogate-assisted evolutionary algorithms have achieved great success in addressing expensive optimization problems, they still suffer from stiff challenges when the number of dimensions of problems becomes large. The primary reason lies in that it is very hard to build an acceptable surrogate model in the high-dimensional search space with small amounts of evaluated historical data in evolution. To tackle this issue, we suggest an effective surrogate modeling method for large-scale expensive optimization in this paper, where the models are built on a number of linear subspaces instead of the original search space. Specifically, a linear subspace is constructed by a pair of points/solutions which are generated based on the set of elite solutions. For each linear subspace, several historical solutions are first associated according to their distance to the linear subspace, and then a surrogate model is trained by the associated solutions and used to evaluate the offspring. To ensure the exploration and exploitation capacity of the proposed method, these linear subspaces and the surrogate models are updated after a few iterations. Experimental results on CEC’2010 and CEC’2013 single-objective optimization problems with up to 1500 decision variables show that the proposed algorithm is superior over six comparison algorithms. Moreover, we also extend the proposed algorithm to multi-objective optimization problems and verified its competitiveness on problems with up to 1500 decision variables. IEEE

关键词： Evolutionary algorithms

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An Empirical Study on Neural Networks Pruning: Trimming for Reducing Memory or Workload 5

An Empirical Study on Neural Networks Pruning: Trimming for ...

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5th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2023

作者： Xiao, Kaiwen Cai, Xiaodong Xu, Ke School of Artificial Intelligence Anhui University Hefei China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei China

ISBN: (纸本)9798350393521

Most of existing studies on neural network pruning only consider memory-based pruning strategies. However pruning for computational workload is often more important in hardware deployments due to a greater focus on model computation reductions. In addition, most pruning schemes restore model accuracy during pruning at the expense of adding hyperparameters, extending training time and training complexity. This work proposes a statistical-based globally soft iterative pruning scheme. With little extra calculation, an extremely sparse model can be obtained without additional hyperparameters and extended training time. Moreover, this work proposes the concept of computational intensity to balance model memory and computational workload during pruning. Focusing on memory orientated pruning, we can achieve 303×, 100× and 25× parameter compression on LeNet-5 (MNIST), VGG (CIFAR-10) and AlexNet (ImageNet) models, respectively. In particular, combined with cluster quantization, the LeNet-5 model parameters can be compressed to 3232×. Focusing on workload orientated pruning, we can reduce the computation by 7}.6× on the AlexNet model, without accuracy loss, significantly higher than prior work. In addition, in order to verify the versatility of the pruning method, we also migrate the pruning task to the object detection and implement 10× parameter compression and 2.8× computation compression for YOLOv2 with reduced mAP within 1%. © 2023 IEEE.

关键词： Iterative methods

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Evolutionary Multi-Objective Neural Architecture Search for Generalized Cognitive Diagnosis Models 5

Evolutionary Multi-Objective Neural Architecture Search for ...

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5th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2023

作者： Yang, Shangshang Zhen, Cheng Tian, Ye Ma, Haiping Liu, Yuanchao Zhang, Panpan Zhang, Xingyi School of Artificial Intelligence Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei China Institutes of Physical Science and Information Technology Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei China Automation for Process Industries Northeastern University State Key Laboratory of Synthetical Shenyang China

ISBN: (纸本)9798350393521

Cognitive diagnosis models (CDMs) with high generalization are essential for intelligent education systems to reveal students' knowledge states in multiple datasets. However, existing CDMs' architectures are designed dependent on researcher expertise and experience from observing and summarizing partial students' learning behaviors, which makes handcrafted models not cover all learning behaviors well and thus limits their generalization. To develop generalized CDMs, this paper proposes an evolutionary neural architecture search to design CDMs' architectures effective on multiple datasets automatically. Specifically, we first formulate the search task as a multi-objective optimization problem (MOP), which maximizes model performance on multiple datasets containing learning behaviors as many as possible to ensure model generalization. Then, an expressive search space is devised to contain as many potential architectures as possible, where each architecture is denoted by a unified form, taking three given inputs and integrating them in a linear or no-linear manner for prediction. Finally, an evolutionary algorithm with a tailored deduplication strategy is employed to solve the MOP, where each architecture is further represented by a single-root computation tree for easy optimization. Experiments on multiple datasets demonstrate the generalization and effectiveness of the best architecture searched by the proposed approach, and the searched architecture also holds as good interpretability as handcrafted architectures. © 2023 IEEE.

关键词： Multiobjective optimization

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Policy iteration-based adaptive optimal control for Markov jump systems: a transition-probability-free asynchronous approach

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Science China(Information Sciences) 2024年第11期67卷 363-364页

作者： Weidi CHENG Chengcheng REN Shuping HE Changyin SUN Key Laboratory of Intelligent Computing and Signal Processing (Ministry of Education) School of Electrical Engineering and Automation Anhui University Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and Automation Anhui University Information Materials and Intelligent Sensing Laboratory of Anhui Province Engineering Research Center of Autonomous Unmanned System Technology (Ministry of Education) School of Artificial Intelligence Anhui University

The controller and filter design problems of Markov jump systems(MJSs) have gained significant attention over the past few decades. These studies include various aspects,including stochastic stabilization [1], optimal tracking control [2], and dissipative filter design [3]. Although numerous publications address the optimal controller design for MJSs, the issue of hidden MJSs, particularly those with mismatched jumping modes between the system and the controller, has rarely been explored.

关键词： Markov processes

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Alignment-Free RGB-T Salient Object Detection: A Large-Scale Dataset and Progressive Correlation Network 39

Alignment-Free RGB-T Salient Object Detection: A Large-Scale...

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39th Annual AAAI Conference on Artificial intelligence, AAAI 2025

作者： Wang, Kunpeng Chen, Keke Li, Chenglong Tu, Zhengzheng Luo, Bin Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University China Anhui Provincial Key Laboratory of Security Artificial Intelligence School of Artificial Intelligence Anhui University China

ISBN: (纸本)157735897X

Alignment-free RGB-Thermal (RGB-T) salient object detection (SOD) aims to achieve robust performance in complex scenes by directly leveraging the complementary information from unaligned visible-thermal image pairs, without requiring manual alignment. However, the labor-intensive process of collecting and annotating image pairs limits the scale of existing benchmarks, hindering the advancement of alignment-free RGB-T SOD. In this paper, we construct a large-scale and high-diversity unaligned RGB-T SOD dataset named UVT20K, comprising 20,000 image pairs, 407 scenes, and 1256 object categories. All samples are collected from real-world scenarios with various challenges, such as low illumination, image clutter, complex salient objects, and so on. To support the exploration for further research, each sample in UVT20K is annotated with a comprehensive set of ground truths, including saliency masks, scribbles, boundaries, and challenge attributes. In addition, we propose a Progressive Correlation Network (PCNet), which models inter- and intra-modal correlations on the basis of explicit alignment to achieve accurate predictions in unaligned image pairs. Extensive experiments conducted on two unaligned three weakly aligned three aligned datasets demonstrate the effectiveness of our method. Copyright © 2025, Association for the Advancement of Artificial intelligence (***). All rights reserved.

关键词： Object detection

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