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arXiv 2024年

作者： Liu, Lingling Wang, Aimin Wu, Jing Lu, Jiao Li, Jiahui Sun, Geng College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China

In this research, a unique approach to provide communication service for terrestrial receivers via using unmanned aerial vehicle-enabled visible light communication is investigated. Specifically, we take into account a unmanned aerial vehicle-enabled visible light communication scenario with multiplex transmitters, multiplex receivers, and a single eavesdropper, each of which is equipped with a single photodetector. Then, a unmanned aerial vehicle deployment multi-objective optimization problem is formulated to simultaneously make the optical power received by receiving surface more uniform, minimize the amount of information collected by a eavesdropper, and minimize the energy consumption of unmanned aerial vehicles, while the locations and transmission power of unmanned aerial vehicles are simultaneously optimized under certain constraints. Since the formulated unmanned aerial vehicle deployment multiobjective optimization problem is complex and nonlinear, it is challenging to be tackled by using conventional methods. For the purpose of solving the problem, a multi-objective evolutionary algorithm based on decomposition with chaos initiation and crossover mutation is proposed. Simulation outcomes show that the proposed approach is superior to other approaches, and is efficient at improving the security and energy efficiency of visible light communication system. Copyright © 2024, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Aspect-Based Sentiment Analysis with Explicit Sentiment Augmentations

arXiv

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arXiv 2023年

作者： Ouyang, Jihong Yang, Zhiyao Liang, Silong Wang, Bing Wang, Yimeng Li, Ximing College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of MOE Jilin University China

Aspect-based sentiment analysis (ABSA), a fine-grained sentiment classification task, has received much attention recently. Many works investigate sentiment information through opinion words, such as "good" and "bad". However, implicit sentiment data widely exists in the ABSA dataset, whose sentiment polarity is hard to determine due to the lack of distinct opinion words. To deal with implicit sentiment, this paper proposes an ABSA method that integrates explicit sentiment augmentations (ABSA-ESA) to add more sentiment clues. We propose an ABSA-specific explicit sentiment generation method to create such augmentations. Specifically, we post-train T5 by rule-based data and employ three strategies to constrain the sentiment polarity and aspect term of the generated augmentations. We employ Syntax Distance Weighting and Unlikelihood Contrastive Regularization in the training procedure to guide the model to generate the explicit opinion words with the same polarity as the input sentence. Meanwhile, we utilize the Constrained Beam Search to ensure the augmentations are aspect-related. We test ABSA-ESA on two ABSA benchmarks. The results show that ABSA-ESA outperforms the SOTA baselines on implicit and explicit sentiment accuracy. Copyright © 2023, The Authors. All rights reserved.

关键词： Sentiment analysis

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VECLLF: A vehicle-edge collaborative lifelong learning framework for anomaly detection in VANETs

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Computer Networks 2025年 265卷

作者： Wang, Yingqing Liang, Yanhua Huang, Yue Qin, Guihe Jilin University College of Computer Science and Technology Changchun130012 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China Jilin University Center for Computer Fundamental Education Changchun130012 China

With the rapid development of intelligent connected vehicles, Vehicular Ad Hoc Network (VANET) is widely used in intelligent transportation systems. VANETs are vulnerable to attacks in their operating environments, such as denial of service (DoS) attacks and injection attacks. Thus, the development of an efficient anomaly detection framework is a promising solution. Most existing methods can only detect known attacks, are less efficient in detecting unknown attacks, and fail to deal with them. To solve these problems, the lifelong learning framework (LLF) is introduced, which aims to store and label suspicious data by applying blockchain and then retrain the model. However, existing LLFs are overly idealized as they do not adequately consider the key issues of label budget and storage space. To improve the practicality of existing methods, this paper proposes a novel LLF based on the existing LLF, called Vehicle Edge Collaborative Lifelong Learning Framework (VECLLF), which can deploy the model update task on the edge server. Specifically, the proposed anomaly detection module based on ensemble active learning is used to achieve high-performance detection under a limited label budget, the proposed incremental learning module based on a sample library continuously learns the model by replaying representative samples within limited storage space, the proposed optimization-based feature selection module is used to remove redundant and interfering features, and the explainable artificial intelligence (XAI) module is used to explain the model to ensure its interpretability. In addition, the blockchain-based database (BC-DB) module is used to help store suspicious data and interact with security agencies to label it. We conducted experiments on two public datasets, AWID2 and CICIDS2017, and the results show that VECLLF outperforms state-of-the-art anomaly detection models and existing LLF, with F1-scores exceeding 97% on both datasets. Furthermore, we collect data from th

关键词： Rapid transit

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Computing PUR of Zero-Dimensional Ideals of Breadth at Most One

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Journal of Systems Science & Complexity 2021年第6期34卷 2396-2409页

作者： PAN Jian SHANG Baoxin LI Zhe ZHANG Shugong School of Mathematics Key Laboratory of Symbolic Computation and Knowledge Engineering(Ministry of Education)Jilin UniversityChangchun 130012China College of Science Northeast Electric Power UniversityJilin 132012China School of Science Changchun University of Science and TechnologyChangchun 130022China

In this paper,for a zero-dimensional polynomial ideal I,the authors prove that k[x_(1),x_(2),…,x_(n)]/I is cyclic if and only if the breadth of I is 0 or ***,the authors present a new algorithm to compute polynomial ... 详细信息

关键词： Cyclic basis Grobner basis polynomial univariate representation separating element

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Affine Invariant Feature Based on Local Color

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Chinese Journal of Electronics 2023年第3期22卷 511-516页

作者： LIU Pingping ZHOU Qiuzhan ZHAO Hongwci School of Computer Science and Technology Jilin University Changchun China Ministry of Education Key Laboratory of Symbolic Computation and Knowledge Engineering Jilin University Changchun China School of Communication Engineering Jilin University Changchun China

Image localization using local features has attracted a lot of attention in recent mobile robots research. A novel, fast local invariant feature in affine transformation is proposed in this article, called AIFLC (Affine invariant feature based on local color). We adopt affine moment invariants to build affine invariant descriptors. Moreover, we use color gradient based center moment instead of original pixel values in order to enhance discriminative power and robustness of descriptor in photometric transformations. Simulation results show that the run time of AIFLC using optimal selection parameters is about 1/3 of classical SIFT algorithm. Using the standard evaluation images and the ones taken by mobile robots, we experimentally demonstrate that the AIFLC outperforms the state-of-art approaches such as SIFT and SURF in terms of image scaling, rotation, viewpoint changing, and blur transformations.

关键词： Location awareness Image color analysis Navigation Simulation Lighting Real-time systems Robustness

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From Incomplete Coarse-Grained to Complete Fine-Grained: A Two-Stage Framework for Spatiotemporal Data Reconstruction

arXiv

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arXiv 2024年

作者： Sun, Ziyu Su, Haoyang Wang, En Yang, Funing Yang, Yongjian Liu, Wenbin 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

With the rapid development of various sensing devices, spatiotemporal data is becoming increasingly important nowadays. However, due to sensing costs and privacy concerns, the collected data is often incomplete and coarse-grained, limiting its application to specific tasks. To address this, we propose a new task called spatiotemporal data reconstruction, which aims to infer complete and fine-grained data from sparse and coarse-grained observations. To achieve this, we introduce a two-stage data inference framework, DiffRecon, grounded in the Denoising Diffusion Probabilistic Model (DDPM). In the first stage, we present Diffusion-C, a diffusion model augmented by ST-PointFormer, a powerful encoder designed to leverage the spatial correlations between sparse data points. Following this, the second stage introduces Diffusion-F, which incorporates the proposed T-PatternNet to capture the temporal pattern within sequential data. Together, these two stages form an end-to-end framework capable of inferring complete, fine-grained data from incomplete and coarse-grained observations. We conducted experiments on multiple real-world datasets to demonstrate the superiority of our method. © 2024, CC BY.

关键词： Spatio-temporal data

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Attribute imputation autoencoders for attribute-missing graphs

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knowledge-Based Systems 2024年 291卷

作者： Xia, Riting Zhang, Chunxu Li, Anchen Liu, Xueyan Yang, Bo Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China School of Artificial Intelligence Jilin University Jilin Changchun130012 China School of Computer Science and Technology Jilin University Jilin Changchun130012 China

Analyzing attribute-missing graphs with a complete topology, but missing the attributes of some nodes, is an emerging and challenging research topic. Data imputation techniques based on graph autoencoders are commonly used for attribute-missing graphs. However, this method cannot effectively integrate existing attributes and structural information during the encoding stage and is prone to introducing noise, resulting in inaccurate imputation. In addition, the expressiveness of decoders in existing methods is limited because their network architecture has not been adequately designed, which restricts the accuracy and robustness of the generated attributes. To address these issues, we propose a novel Attribute Imputation AutoEncoder for attribute-missing graphs, named AIAE. In particular, during the encoding stage, a dual encoder based on knowledge distillation is designed to encode both attribute and structural information into representations of attribute-missing nodes to achieve more accurate imputation. To avoid introducing noise, we fully exploit the observed information by reorganizing the representations of the attribute-missing and attribute-observed nodes. In the decoding stage, we propose a multi-scale decoder with masking to make the decoder more expressive and enhance its robustness and generative ability. Extensive experiments demonstrate that our model significantly outperforms state-of-the-art methods in attribute-missing graphs. © 2024 Elsevier B.V.

关键词： Topology

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Deep Feature-Based Text Clustering and Its Explanation 39

Deep Feature-Based Text Clustering and Its Explanation

引用

39th IEEE International Conference on Data engineering, ICDE 2023

作者： Guan, Renchu Zhang, Hao Liang, Yanchun Giunchiglia, Fausto Huang, Lan Feng, Xiaoyue Jilin University Key Laboratory for Symbol Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Changchun China University of Trento Disi Trento Italy

ISBN: (纸本)9798350322279

Text clustering is a critical step in text data analysis and has been extensively studied by the text mining community. Most existing text clustering algorithms are based on the bag-of-words model, which faces the high-dimensional and sparsity problems and ignores text structural and sequence information. Deep learning-based models such as convolutional neural networks and recurrent neural networks regard texts as sequences but lack supervised signals and explainable results. In this paper, we propose a deep feature-based text clustering (DFTC) framework that incorporates pretrained text encoders into text clustering tasks. This model, which is based on sequence representations, breaks the dependency on supervision. The experimental results show that our model outperforms classic text clustering algorithms on almost all the considered datasets. In addition, the explanation of the clustering results is significant for understanding the principles of the deep learning approach. Our proposed clustering framework includes an explanation module that can help users understand the meaning and quality of the clustering results. Our code is available at https://***/KEAML-JLU/DeepTextClustering. © 2023 IEEE.

关键词： Cluster analysis

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Reusable Generator Data-Free knowledge Distillation with Hard Loss Simulation for Image Classification

SSRN

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SSRN 2024年

作者： Sun, Yafeng Wang, Xingwang Huang, Junhong Chen, Shilin Hou, Minghui 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

In many image classification scenarios where knowledge distillation (KD) is applied, multiple users need to train various student models that conform to the device's computational limitations at different times. However, due to security concerns, existing data-free KD (DFKD) methods discard the trained generator after completing a task, preventing its reuse in training subsequent students and resulting in wasted resources. Furthermore, the unlabeled nature of fake images eliminates the hard loss that plays a crucial role in data-driven KD. This paper introduces the reusable generator DFKD with hard loss simulation (RG-HLS) to address these challenges. RG-HLS employs a generator consisting of two generative networks and a decoder. The decoder and one of the generative networks can be reused in subsequent tasks. The adversarial example attack is also introduced to mimic the behavior of the teacher model in predicting real images, ensuring that DFKD exhibits a hard loss similar to that of data-driven KD. Extensive experiments across CIFAR-10, CIFAR-100, and Tiny-ImageNet datasets validate the proposed method's superior performance. The reusable generator accelerates convergence without compromising security, and integrating the hard loss further boosts accuracy. Code is available at https://***/sunyafeng-jlu/RG-HLS. © 2024, The Authors. All rights reserved.

关键词： Image classification

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DeepHBSP:A Deep Learning Framework for Predicting Human Blood-Secretory Proteins Using Transfer Learning

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Journal of Computer Science & Technology 2021年第2期36卷 234-247页

作者： Wei Du Yu Sun Hui-Min Bao Liang Chen Ying Li Yan-Chun Liang Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education College of Computer Science and TechnologyJilin UniversityChangchun 130012China Department of Computer Science College of EngineeringShantou UniversityShantou 515063China Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Zhuhai College of Jilin University Zhuhai 519041China

The identification of blood-secretory proteins and the detection of protein biomarkers in the blood have an important clinical application *** methods for predicting blood-secretory proteins are mainly based on traditional machine learning algorithms,and heavily rely on annotated protein *** traditional machine learning algorithms,deep learning algorithms can automatically learn better feature representations from raw data,and are expected to be more promising to predict blood-secretory *** present a novel deep learning model(DeepHBSP)combined with transfer learning by integrating a binary classification network and a ranking network to identify blood-secretory proteins from the amino acid sequence information *** loss function of DeepHBSP in the training step is designed to apply descriptive loss and compactness loss to the binary classification network and the ranking network,*** feature extraction subnetwork of DeepHBSP is composed of a multi-lane capsule ***,transfer learning is used to train a highly accurate generalized model with small samples of blood-secretory *** main contributions of this study are as follows:1)a novel deep learning architecture by integrating a binary classification network and a ranking network is proposed,superior to existing traditional machine learning algorithms and other state-of-the-art deep learning architectures for biological sequence analysis;2)the proposed model for blood-secretory protein prediction uses only amino acid sequences,overcoming the heavy dependence of existing methods on annotated protein features;3)the blood-secretory proteins predicted by our model are statistically significant compared with existing blood-based biomarkers of cancer.

关键词： blood-secretory protein deep learning capsule network transfer learning

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