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Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Xiaosong Han Chengyang Zhou Yanchun Liang Renchu Guan Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology Jilin University Changchun China Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology Zhuhai Laboratory of Key Laboratory for Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University Zhuhai College of Science and Technology Zhuhai China

Particle Swarm Optimization with Migration (MPSO) is proposed to solve the issue that PSO will encounter unbearable time cost problems when dealing with High-dimension, Expensive and Black-box objective function tasks. The Migration operator is inspired by the migration of Salmon. Salmon will start a dangerous journey from the ocean to the home rivers for reproduction. The process of the entire behavior is similar to the reduction and recovery of dimension. Therefore, we design the Migration operator where a pretrained Wasserstein Autoencoders (WAE) is applied to simulate the migration behavior to accelerate the process of evolution in PSO, and we use Least-Squares Regression in lower space to produce better generation. In comparison with famous baseline methods in some benchmark functions, MPSO converges faster and more accurately, which shows the great potential of migration operations.

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Deep Feature-Based Text Clustering and Its Explanation 39

Deep Feature-Based Text Clustering and Its Explanation

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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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Optimizing simple tabular reduction with a bitwise representation 25

Optimizing simple tabular reduction with a bitwise represent...

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25th International Joint Conference on Artificial Intelligence, IJCAI 2016

作者： Wang, Ruiwei Xia, Wei Yap, Roland H.C. Li, Zhanshan School of Software Jilin University Changchun China Key Laboratory of Symbol Computation and Knowledge Engineering Education Ministry China School of Computing National University of Singapore Singapore

Maintaining Generalized Arc Consistency (GAC) during search is considered an efficient way to solve non-binary constraint satisfaction problems. Bit-based representations have been used effectively in Arc Consistency algorithms. We propose STRbit, a GAC algorithm, based on simple tabular reduction (STR) using an efficient bit vector support data structure. STRbit is extended to deal with compression of the underlying constraint with c-tuples. Experimental evaluation show our algorithms are faster than many algorithms (STR2, STR2-C, STR3, STR3-C and MDDc) across a variety of benchmarks except for problems with small tables where complex data structures do not payoff.

关键词： Data structures

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Time and Time-Frequency Features Integrated CNN Model for Heart Sound Signals Detection

Time and Time-Frequency Features Integrated CNN Model for He...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Ren, Zhimin Qiao, Yuheng Yuan, Yuping Zhou, You Liang, Yanchun Shi, Xiaohu College of Computer Science and Technology of Jilin University Changchun130012 China Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Changchun130012 China Zhuhai College of Science and Technology School of Computer Science Zhuhai519041 China

ISBN: (纸本)9781665468190

Automatic heart sound diagnosis plays an important role in the early detection of cardiovascular diseases. Phonocardiogram (PCG) signals are often used in this field f or its low cost and non-invasive advantages. In this paper, we design a new time and time-frequency features integrated CNN (TTFI-CNN) model for heart sound signals detection. In the model, a 1D CNN and a BiLSTM are combined into 1D CRNN module to extract temporal features from the original PCG signal, and a 2D CNN module is applied to capture high-level features from time-frequency domain MFCCs inputs. The outputs of the two modules are recalibrated by attention mechanism to selectively emphasize informative features and suppress less useful ones. To verify the proposed TTFI-CNN model, it is applied to two public datasets with different classification t asks (binary and multiclassification). The TTFI-CNN model has achieved 97.15% accuracy, 97.13% sensitivity, and 97.17% specificity on physionet/cinc database, and obtained 3.31 and 2.61 precision scores on the PASCAL database A and B, respectively. Compared with the previous state-of-the-art methods, the TTFI-CNN performs best on all the above metrics. https://***/XxxNnnSssWww/TTFI-CNN. © 2022 IEEE.

关键词： Signal detection

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Hilbert-huang transform-based local regions descriptors

Hilbert-huang transform-based local regions descriptors

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2007 18th British Machine Vision Conference, BMVC 2007

作者： Han, Dongfeng Li, Wenhui Guo, Wu Li, Zongcheng Computer Science and Technology Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University Changchun China School of Engineering Technology Shandong University of Technology Zibo China

ISBN: (纸本)1901725340

This paper presents a new interest local regions descriptors method based on Hilbert-Huang Transform. The neighborhood of the interest local region is decomposed adaptively into oscillatory components called intrinsic mode functions (IMFs). Then the Hilbert transform is applied to each component and get the phase and amplitude information. The proposed descriptors samples the phase angles information and amalgamates them into 10 overlap squares with 8-bin orientation histograms. The experiments show that the proposed descriptors are better than SIFT and other standard descriptors. Essentially, the Hilbert-Huang Transform based descriptors can belong to the class of phase-based descriptors. So it can provides a better way to overcome the illumination changes. Additionally, the Hilbert-Huang transform is a new tool for analyzing signals and the proposed descriptors is a new attempt to the Hilbert-Huang transform.

关键词： Hilbert-Huang transform

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Checking linearizability with fine-grained traces 16

Checking linearizability with fine-grained traces

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31st Annual ACM Symposium on Applied Computing, SAC 2016

作者： Long, Zhenyue Zhang, Yu State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China Laboratory of Symbol Computation and Knowledge Zhuhai College Jilin University China Information Center Guangdong Power Grid Co. Ltd. China

ISBN: (纸本)9781450337397

Linearizability is an important correctness criterion for concurrent objects and automatic checking of linearizability often involves searching a sequential witness in an exponentially growing space of traces. We present two acceleration techniques for checking linearizability based on fine-grained traces: one is based on speculation of linearization points, and the other is to separate read-only methods from the enumeration of serial witnesses. We have applied them to checking linearizability of eight different algorithms and the experiment shows that both can offer significant speed-up, allowing us to explore further on error analysis of concurrent objects based on fine-grained traces. © 2016 ACM.

关键词： fine-grained trace automatic verification linearizability

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PSO based partitional clustering algorithm

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Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (engineering and Technology Edition) 2008年第6期38卷 1371-1377页

作者： Zhang, Chang-Sheng Sun, Ji-Gui Cui, Yan Yang, Feng-Qin Key Laboratory of Symbol Computation and Knowledge Engineering College of Computer Science and Technology Jilin University Changchun 130012 China

To obtain the optimal partition of a data set, a hybrid clustering algorithm, PKPSO, based on PSO is proposed. In the proposed PKPSO the PSO algorithm is effectively integrated with the K means algorithm. Among the population, selected candidate solutions are further optimized to improve the accuracy by the K-means algorithm. By analyzing the algorithm, the criterions for control parameters selection are determined. Partional clustering result by the proposed PKPSO is compared with that by PSO or by K-means algorithm, and results show that the global convergent property of PKPSO is better than that of the other algorithms. The PKPSO can not only overcome the shortcoming of local minimum trapping of the K-means, but also the solution precision and algorithm stability are better than that of the other two algorithm.

关键词： Clustering algorithms

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Traffic optimization for distributed network model base on Petri Nets

Traffic optimization for distributed network model base on P...

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Japan-China Joint Workshop on Frontier of Computer Science and Technology (FCST)

作者： Chang, Xin Pang, Huanli Hu, Liang Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education Jilin University Changchun 130012 China Collage of Computer Science and Engineering Changchun University of Technology Changchun 130012 China

ISBN: (纸本)9780769541396

Petri Nets is a powerful mathematical modeling tool for system description and analysis, with which we can describe the relationship among entities efficiently. The present thesis puts forward the modeling of the distributed computer network DNMPN and DNMPNTO based on Petri Nets. DNMPN implements the basic function of distributed computer network, and DNMPNTO modifies DNMPN's repetitive forward problem. Thus, it is efficiently helpful to optimize the unit traffic of the network, save the net work resource and improve the efficiency of the network system. © 2010 IEEE.

关键词： Petri nets

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A multi-heuristic cooperative ant colony system for optimizing elimination ordering of Bayesian networks

A multi-heuristic cooperative ant colony system for optimizi...

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2010 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT 2010

作者： Dong, Xuchu Ouyang, Dantong Ye, Yuxin Yu, Haihong Zhang, Yonggang Department of Computer Science and Technology Jilin University Changchun Jilin 130012 China Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Changchun Jilin 130012 China

ISBN: (纸本)9780769541914

To solve the problem of searching for an optimal elimination ordering of Bayesian networks, a novel effective heuristic, MinSumWeight, and an ACS approach incorporated with multi-heuristic mechanism are proposed. The ACS approach named MHC-ACS utilizes a set of heuristics to direct the ants moving in the search space. The cooperation of multiple heuristics helps ants explore more regions. Moreover, the most appropriate heuristic will be identified and be reinforced with the evolution of the whole system. Experiments demonstrate that MHC-ACS has a better performance than other swarm intelligence methods. © 2010 IEEE.

关键词： Bayesian networks

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SIoTFog:Byzantine-resilient IoT fog networking

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Frontiers of Information Technology & Electronic engineering 2018年第12期19卷 1546-1557页

作者： Jian-wen XU Kaoru OTA Mian-xiong DONG An-feng LIU Qiang LI Department of Information and Electronic Engineering Muroran Institute of Technology School of Information Science and Engineering Central South University MOE Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University

The current boom in the Internet of Things(IoT) is changing daily life in many ways, from wearable devices to connected vehicles and smart cities. We used to regard fog computing as an extension of cloud computing, but it is now becoming an ideal solution to transmit and process large-scale geo-distributed big data. We propose a Byzantine fault-tolerant networking method and two resource allocation strategies for IoT fog computing. We aim to build a secure fog network, called "SIoTFog," to tolerate the Byzantine faults and improve the efficiency of transmitting and processing IoT big data. We consider two cases, with a single Byzantine fault and with multiple faults, to compare the performances when facing different degrees of risk. We choose latency, number of forwarding hops in the transmission, and device use rates as the metrics. The simulation results show that our methods help achieve an efficient and reliable fog network.

关键词： Byzantine fault tolerance Fog computing Resource allocation Internet of Things(IoT)

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