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arXiv 2023年

作者： Ji, Qiu Zhang, Xiaoping Ye, Yuxin Qi, Guilin Li, Jiaye Li, Site Ren, Jianjie Lu, Songtao School of Modern Posts Institute of Modern Posts Nanjing University of Posts and Telecommunications Nanjing China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China National Data Center ofTraditional Chinese MedicineChina Academy of ChineseMedical Sciences Beijing100700 China College of Computer Science and Technology Jilin University Changchun130012 China School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing China School of Mathematics Southeast University Nanjing China Chien-Shiung Wu College Southeast University Nanjing China

Ontology revision aims to seamlessly incorporate a new ontology into an existing ontology and plays a crucial role in tasks such as ontology evolution, ontology maintenance, and ontology alignment. Similar to repair single ontologies, resolving logical incoherence in the task of ontology revision is also important and meaningful, because incoherence is a main potential factor to cause inconsistency and reasoning with an inconsistent ontology will obtain meaningless answers. To deal with this problem, various ontology revision approaches have been proposed to define revision operators and design ranking strategies for axioms in an ontology. However, they rarely consider axiom semantics which provides important information to differentiate axioms. In addition, pre-trained models can be utilized to encode axiom semantics, and have been widely applied in many natural language processing tasks and ontology-related ones in recent years. Therefore, in this paper, we study how to apply pre-trained models to revise ontologies. We first define four scoring functions to rank axioms based on a pre-trained model by considering various information from an ontology. Based on the functions, an ontology revision algorithm is then proposed to deal with unsatisfiable concepts at once. To improve efficiency, an adapted revision algorithm is designed to deal with unsatisfiable concepts group by group. We conduct experiments over 1. ontology pairs and compare our algorithms and scoring functions with existing ones. According to the experiments, our algorithms could achieve promising performance. The adapted revision algorithm could improve the efficiency largely, and at most about 90% of the time could be saved for some ontology pairs. Some of our scoring functions like reliableOnt cos could help a revision algorithm obtain better results in many cases, especially for those challenging ontology pairs like OM8. We also provide discussion about the overall experimental results and guidelin

关键词： Ontology

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Multi-objective Aerial Collaborative Secure Communication Optimization via Generative Diffusion Model-enabled Deep Reinforcement Learning

arXiv

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arXiv 2024年

作者： Zhang, Chuang Sun, Geng Li, Jiahui Wu, Qingqing Wang, Jiacheng Niyato, Dusit Liu, Yuanwei The College of Computer Science and Technology Jilin University Changchun130012 China The Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China The College of Computer Science and Technology Jilin University Changchun130012 China The College of Computing and Data Science Nanyang Technological University 639798 Singapore The Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China The College of Computing and Data Science Nanyang Technological University 639798 Singapore The School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom

Due to flexibility and low-cost, unmanned aerial vehicles (UAVs) are increasingly crucial for enhancing coverage and functionality of wireless networks. However, incorporating UAVs into next-generation wireless communication systems poses significant challenges, particularly in sustaining high-rate and long-range secure communications against eavesdropping attacks. In this work, we consider a UAV swarm-enabled secure surveillance network system, where a UAV swarm forms a virtual antenna array to transmit sensitive surveillance data to a remote base station (RBS) via collaborative beamforming (CB) so as to resist mobile eavesdroppers. Specifically, we formulate an aerial secure communication and energy efficiency multi-objective optimization problem (ASCEE-MOP) to maximize the secrecy rate of the system and to minimize the flight energy consumption of the UAV swarm. To address the non-convex, NP-hard and dynamic ASCEE-MOP, we propose a generative diffusion model-enabled twin delayed deep deterministic policy gradient (GDMTD3) method. Specifically, GDMTD3 leverages an innovative application of diffusion models to determine optimal excitation current weights and position decisions of UAVs. The diffusion models can better capture the complex dynamics and the trade-off of the ASCEE-MOP, thereby yielding promising solutions. Simulation results highlight the superior performance of the proposed approach compared with traditional deployment strategies and some other deep reinforcement learning (DRL) benchmarks. Moreover, performance analysis under various parameter settings of GDMTD3 and different numbers of UAVs verifies the robustness of the proposed approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Energy efficiency

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Enhanced Doubly Robust Learning for Debiasing Post-Click Conversion Rate Estimation

arXiv

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arXiv 2021年

作者： Guo, Siyuan Zou, Lixin Liu, Yiding Ye, Wenwen Cheng, Suqi Wang, Shuaiqiang Chen, Hechang Yin, Dawei Chang, Yi School of Artificial Intelligence Jilin University China Baidu Inc. China International Center of Future Science Jilin University China Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education Jilin University China

Post-click conversion, as a strong signal indicating the user preference, is salutary for building recommender systems. However, accurately estimating the post-click conversion rate (CVR) is challenging due to the selection bias, i.e., the observed clicked events usually happen on users’ preferred items. Currently, most existing methods utilize counterfactual learning to debias recommender systems. Among them, the doubly robust (DR) estimator has achieved competitive performance by combining the error imputation based (EIB) estimator and the inverse propensity score (IPS) estimator in a doubly robust way. However, inaccurate error imputation may result in its higher variance than the IPS estimator. Worse still, existing methods typically use simple model-agnostic methods to estimate the imputation error, which are not sufficient to approximate the dynamically changing model-correlated target (i.e., the gradient direction of the prediction model). To solve these problems, we first derive the bias and variance of the DR estimator. Based on it, a more robust doubly robust (MRDR) estimator has been proposed to further reduce its variance while retaining its double robustness. Moreover, we propose a novel double learning approach for the MRDR estimator, which can convert the error imputation into the general CVR estimation. Besides, we empirically verify that the proposed learning scheme can further eliminate the high variance problem of the imputation learning. To evaluate its effectiveness, extensive experiments are conducted on a semi-synthetic dataset and two real-world datasets. The results demonstrate the superiority of the proposed approach over the state-of-the-art methods. The code is available at https://***/guosyjlu/MRDR-DL. Copyright © 2021. The Authors. All rights reserved.

关键词： Recommender systems

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Elucidating Spatial Complex Structures from Mass Spectrometry Imaging with Deep Multimodal Model

Elucidating Spatial Complex Structures from Mass Spectrometr...

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Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Jiangnan Cui Junjie Xia Xuan Li Yan Wang Fufang Qiu Yaochen Xu Keren Xu Chunman Zuo School of Computer Science and Technology Institute of Artificial Intelligence Donghua University Shanghai China Institute of Artificial Intelligence Donghua University Shanghai China College of Computer Science and Technology Jilin University Changchun China Department of Neurosurgery Huashan Hospital Shanghai China CAS Key Laboratory of Systems Biology Shanghai Institute of Biochemistry and Cell Biology Center for Excellence in Molecular Cell Science Chinese Academy of Sciences Shanghai China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China

Spatial mass spectrometry imaging (SMSI) technology has enabled the characterization of biomolecule patterns within the tissue microenvironment, yet its analyses suffer from substantial noise. The lack of effective methods for exploiting multi-view features (e.g., spatial location) has hindered their elucidation ability for tissue heterogeneity. Here, we propose MSG, a multimodal modal that integrates histology, spatial location, and MSI data, in deciphering tissue structure by graph neural network. Specifically, MSG learns low-dimensional representations by aggregating histomorphological features from neighboring tiles via graph attention autoencoder. MSG outperforms other methods in identifying spatial domains and denoising data. The subsequent domain-guided differential expression analysis of the denoised data enables the identification of ions with significantly enriched patterns within the recognized domains. MSG exhibits its versatility in handling SMSI data from various platforms.

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BERST: An Engine and Tool for Exploring Biomedical Entities and Relationships

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Chinese Journal of Electronics 2019年第4期28卷 797-804页

作者： BAI Tian GE Yan YANG Changqing LIU Xiaohua GONG Leiguang WANG Ye HUANG Lan College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Yantai Intelligent Information Technologies Ltd. Department of Computer Science and Technology Zhuhai College of Jilin University

To facilitate the search of rapidly growing biomedical knowledge in literature, we developed a Biomedical entity-relationship search tool(BERST). It is also a biomedical knowledge integration framework, which presently contains six popular databases represented in terms of a network of concepts and relations extracted from these knowledge sources. Users search the integrated knowledge network by entering keywords, and BERST returns a sub-network matching and representing the keywords and their relationships. The resulting graph can be navigated interactively allowing users to explore specific paths between any two nodes representing potentially interesting relationships between them. A graphical UI was developed to provide a more intuitive and overall view of the information being searched and studied. BERST framework can be naturally expanded to integrate other biomedical knowledge sources. BERST is implemented as a Java web application.

关键词： Biomedical ontology Relationship mining Relationship searching tool

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Vertex-Disjoint Paths in BCDC Networks

Vertex-Disjoint Paths in BCDC Networks

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International Symposium on Parallel Architectures, Algorithms and Programming (PAAP)

作者： Xi Wang Jianxi Fan Suzhou Institute of Industrial Technology Suzhou China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun P.R. China School of Computer Science and Technology Soochow University Suzhou China

ISBN: (纸本)9781665496407

For any integer $n\geq 2,\ B_{n}$ , the $n$ -dimensional BCDC network, based on a class of BC networks, crossed cube, is a server-centric and high-performance data center network for mega data centers with manageable cabling complexity. Implementing vertex-disjoint paths has great value of application in fault-tolerance and multi-path routing of data center networks. In this paper, we construct $n$ vertex-disjoint paths joining every two distinct vertices in $B_{n}$ , whose maximum length is limited by $\lceil\frac{n}{2}\rceil+5$ , where the diameter of $B_{n}$ is $\lceil\frac{n+1.{2}\rceil+1. . Our results are fundamental and essential for fault-tolerance and multi-path routing in BCDC networks.

关键词： Data centers Fault tolerance Fault tolerant systems Programming Routing Parallel architectures Complexity theory

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Msa-Net: A Medical Image Segmentation Network Based on Spatial Pyramid and Attention Mechanism

SSRN

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SSRN 2023年

作者： Feng, Yuncong Zhu, Xiaoyan Li, Yang Zhang, Xiaoli Lu, Huimin College of Computer Science and Engineering Changchun University of Technology Jilin Changchun130012 China Artificial Intelligence Research Institute Changchun University of Technology Jilin Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Jilin Changchun130012 China Academy for Advanced Interdisciplinary Studies Northeast Normal University Jilin Changchun130024 China Jilin Provincial Smart Health Joint Innovation Laboratory for the New Generation of AI 3000 North Yuanda Avenue Gaoxin North District Changchun130102 China

Medical image segmentation is an important way to assist doctors to accurately diagnose diseases. However, semantic features are difficult to be fully extracted due to the complexity of medical image lesion tissue features (e.g., size, location, and morphology) and the high similarity between the background and the target region. To tackle these problems, we propose a novel medical image segmentation network MSA-Net based on the spatial pyramid and attention mechanism. This technique facilitates the extraction of multi-scale spatial information and the establishment of channel dependencies within images. By incorporating efficient pyramid attention and channel-spatial dual attention modules, the network autonomously focuses on lesion regions while suppressing background interference. Additionally, the SPCA (Spatial Pyramid-Coordinate Attention) module is integrated at the bottleneck layer, enabling the assimilation of larger receptive field information and prioritizing relevant features. This augmentation improves the network's ability to gather both location and channel-based insights. We proceed to evaluate our model qualitatively and quantitatively across four public datasets: ISIC201., Lung segmentation, Kvasir-SEG, and ISLES2022. The results substantiate the superior performance of our proposed model. © 2023, The Authors. All rights reserved.

关键词： Deep learning

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An Enhanced Attentive Implicit Relation Embedding for Social Recommendation

SSRN

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SSRN 2022年

作者： Ma, Xintao Dong, Liyan Wang, Yuequn Li, Yongli Liu, Zhen Zhang, Hao 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 School of Information Science and Technology Northeast Normal University Changchun130117 China Graduate School of Engineering Nagasaki Institute of Applied Science 536 Aba-machi Nagasaki851-0193 Japan

Nowadays, recommendation system incorporating social networks has attracted a lot of research attention and is widely used to understand user preferences regarding social relations. Besides, graph neural networks(GNNs) have proven to learn graph-structured data effectively. Thus, integrating GNN into social recommendations has become one challenge. Many approaches encode both the user-item interaction and social relations to learn user preference. However, the implicit relations are also crucial to understanding the features of users and items. Thus, we propose the attentive implicit relation embedding for social recommendation(SR-AIR), which models the user-item interaction and social networks, utilizing a graph attention mechanism on implicit relations of users and items. We evaluate our framework on two real-world datasets and demonstrate that our framework performs the best compared with state-of-the-art baselines © 2022, The Authors. All rights reserved.

关键词： Recommender systems

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Curvature regularization to prevent distortion in graph embedding 34

Curvature regularization to prevent distortion in graph embe...

引用

34th Conference on Neural Information Processing Systems, NeurIPS 2020

作者： Pei, Hongbin Wei, Bingzhe Chang, Kevin Chen-Chuan Zhang, Chunxu Yang, Bo College of Computer Science and Technology Jilin University China Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign United States Department of Computer Science University of Illinois Urbana-Champaign United States Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education China

Recent research on graph embedding has achieved success in various applications. Most graph embedding methods preserve the proximity in a graph into a manifold in an embedding space. We argue an important but neglected problem about this proximity-preserving strategy: Graph topology patterns, while preserved well into an embedding manifold by preserving proximity, may distort in the ambient embedding Euclidean space, and hence to detect them becomes difficult for machine learning models. To address the problem, we propose curvature regularization, to enforce flatness for embedding manifolds, thereby preventing the distortion. We present a novel angle-based sectional curvature, termed ABS curvature, and accordingly three kinds of curvature regularization to induce flat embedding manifolds during graph embedding. We integrate curvature regularization into five popular proximity-preserving embedding methods, and empirical results in two applications show significant improvements on a wide range of open graph datasets. © 2020 Neural information processing systems foundation. All rights reserved.

关键词： Topology

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A two-stage HV-driven adaptive multi-objective evolutionary algorithm and its application in fixed polarity reed-muller circuits

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Expert Systems with Applications 2025年 288卷

作者： Lu Yang Shengsheng Wang Ruyi Dong Zihao Fu College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 China College of Information and Control Engineering Jilin Institute of Chemical Technology Changchun 130012 China

To achieve a balance between convergence and diversity, we proposed a two-stage HV-driven adaptive multi-objective evolutionary algorithm (TSAMEA). TSAMEA employs a sinusoidal decreasing parameter adjustment method to enhance exploration pace in the first stage. An adaptive parameter control mechanism utilizes historical memory pools and an HV-driven degree adjustment strategy to achieve better exploitation in the second stage. Extensive experimental data demonstrate that TSAMEA outperforms nine other compared MOEAs. The component analysis illustrates the efficacy of each component of TSAMEA. In addition, area and power optimization are now the main limitations in chip design, TSAMEA is applied to area and power optimization for Fixed Polarity Reed-Muller (FPRM) logic circuits and perform well, which further verifies the ability of the TSAMEA to solve practical problems.

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