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International IEEE Conference on Intelligent Systems, IS

作者： Jin-Mao Wei Shu-Qin Wang Jing Wang Jun-Ping You Institute of Computational Intelligence Northeast Normal University China Open Symbol Computation and Knowledge Engineering Laboratory of State Education Jilin University Changchun Jilin China School of Mathematics & Statistics Northeast Normal University Changchun Jilin China

Structural similarity computation plays a crucial role in many applications such as in searching similar documents, in comparing chemical compounds, in finding genetic similarities, etc. We propose in this paper to use structural information content (SIC) for measuring structural information, considering both the nodes and edges of trees. We utilize a binary encoding approach for assigning the weights of different layer nodes and determining if some tree is a subtree of another tree. By defining a fast kernel and recursively computing SICs, we evaluate the structural information similarities of data trees to pattern trees. In the paper, we present the algorithm for calculating SICs with computation complexity of O(n), and use simple examples to instantiate the performance of the proposed method

关键词： Kernel Chemical compounds Silicon carbide XML Genetics Encoding Clustering algorithms Intelligent systems Intelligent structures Pattern analysis

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An Enhanced Approach for Few-Shot Segmentation Via Smooth Downsampling Mask and Label Smoothing Loss

SSRN

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SSRN 2024年

作者： Jin, Hailong Li, Huiying Jilin University College of Computer Science and Technology Jilin Province Changchun China Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Changchun130012 China

Few-shot semantic segmentation aims to segment new categories with only a small number of annotated images. Previous methods mainly focused on exploiting the pixel-level correlation between the support image and the query image, combined with attention-based methods, resulting in significant advancements. In this paper, we introduce a new perspective to enhance few-shot segmentation. We identify that utilizing the bilinear interpolation method to downsample the mask leads to the loss of fine-grained information from the target features. To address this issue, we propose a Smooth Downsampling Mask (SDM) method. The SDM method is designed to retain more effective target semantic features by employing a cascaded downsampling approach with a smooth kernel for mask processing. Additionally, we propose a label smoothing loss to further enhance the performance, which provides direct guidance for low-resolution feature map optimization. Notably, our proposed method involves no additional learnable parameters and computes efficiently. To validate the effectiveness of our approach, we conduct extensive experiments on three public benchmarks, PASCAL-5i, COCO-20i, and FSS-1000, and achieve considerable improvements. © 2024, The Authors. All rights reserved.

关键词： Semantics

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Nondestructive quantitative analysis of compound paracetamol and diphenhydramine hydrochloride powder using RBF networks

Nondestructive quantitative analysis of compound paracetamol...

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International Conference on Machine Learning and Cybernetics, ICMLC 2005

作者： Zheng, Hong Wan, Li.-Ming Jiang, Jing-Qing Liang, Yan-Chun Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Changchun 130012 China College of Computer Science and Engineering Changchun University of Technology Changchun 130012 China Institute of General Development and Evaluation of Equipment EAAF of PLA Beijing 100076 China College of Mathematics and Computer Science Inner Mongolia University for Nationalities Tongliao 028043 China

ISBN: (纸本)078039092X

A method for simultaneous analysis of the two components of compound paracetamol and diphenhydramine hydrochloride powdered drugs on near-infrared (NIR) spectroscopy is developed by using a Radial Basis Function (RBF) network. Nearest neighbor-clustering algorithm is used as the learning algorithm of RBF network. Comparisons of the results obtained from the RBF models with those from BP models show that it is feasible to use the RBF network in nondestructive quantitative analysis of the components of drugs. © 2005 IEEE.

关键词： Neural networks

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Independently Keypoint Learning for Small Object Semantic Correspondence

SSRN

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SSRN 2024年

Semantic correspondence remains a challenging task for establishing correspondences between a pair of images with the same category or similar scenes due to the large intra-class appearance. In this paper, we introduce a novel problem called 'Small Object Semantic Correspondence (SOSC).' This problem is challenging due to the close proximity of keypoints associated with small objects, which results in the fusion of these respective features. It is difficult to identify the corresponding key points of the fused features, and it is also difficult to be recognized. To address this challenge, we propose the Keypoint Bounding box-centered Cropping (KBC) method, which aims to increase the spatial separation between keypoints of small objects, thereby facilitating independent learning of these keypoints. The KBC method is seamlessly integrated into our proposed inference pipeline and can be easily incorporated into other methodologies, resulting in significant performance enhancements. Additionally, we introduce a novel framework, named KBCNet, which serves as our baseline model. KBCNet comprises a Cross-Scale Feature Alignment (CSFA) module and an efficient 4D convolutional decoder. The CSFA module is designed to align multi-scale features, enriching keypoint representations by integrating fine-grained features and deep semantic features. Meanwhile, the 4D convolutional decoder, based on efficient 4D convolution, ensures efficiency and rapid convergence. To empirically validate the effectiveness of our proposed methodology, extensive experiments are conducted on three widely used benchmarks: PF-PASCAL, PF-WILLOW, and SPair-71k. Our KBC method demonstrates a substantial performance improvement of 7.5\% on the SPair-71K dataset, providing compelling evidence of its efficacy. © 2024, The Authors. All rights reserved.

关键词： Convolution

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Independently Keypoint Learning for Small Object Semantic Correspondence

arXiv

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arXiv 2024年

Semantic correspondence remains a challenging task for establishing correspondences between a pair of images with the same category or similar scenes due to the large intra-class appearance. In this paper, we introduce a novel problem called’Small Object Semantic Correspondence (SOSC).’ This problem is challenging due to the close proximity of keypoints associated with small objects, which results in the fusion of these respective features. It is difficult to identify the corresponding key points of the fused features, and it is also difficult to be recognized. To address this challenge, we propose the Keypoint Bounding box-centered Cropping (KBC) method, which aims to increase the spatial separation between keypoints of small objects, thereby facilitating independent learning of these keypoints. The KBC method is seamlessly integrated into our proposed inference pipeline and can be easily incorporated into other methodologies, resulting in significant performance enhancements. Additionally, we introduce a novel framework, named KBCNet, which serves as our baseline model. KBCNet comprises a Cross-Scale Feature Alignment (CSFA) module and an efficient 4D convolutional decoder. The CSFA module is designed to align multi-scale features, enriching keypoint representations by integrating fine-grained features and deep semantic features. Meanwhile, the 4D convolutional decoder, based on efficient 4D convolution, ensures efficiency and rapid convergence. To empirically validate the effectiveness of our proposed methodology, extensive experiments are conducted on three widely used benchmarks: PF-PASCAL, PF-WILLOW, and SPair-71k. Our KBC method demonstrates a substantial performance improvement of 7.5% on the SPair-71K dataset, providing compelling evidence of its efficacy. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolution

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Deep Feature-Based Text Clustering and Its Explanation

Deep Feature-Based Text Clustering and Its Explanation

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International Conference on Data engineering

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

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.

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A hybrid discrete particle swarm algorithm for open-shop problems

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6th International Conference Simulated Evolution and Learning, SEAL 2006

作者： Qingyun, Yang Jigui, Sun Juyang, Zhang Chunjie, Wang College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory for Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 China Open Laboratory for Intelligence Information Processing Fudan University Shanghai 200433 China Basic Sciences of ChangChun University of Technology ChangChun University of Technology Changchun 130012 China

ISBN: (纸本)3540473319

A hybrid discrete particle swarm algorithm is presented in this paper to solve open-shop problems. The operations are redefined in the discrete particle swarm algorithm. To improve the performance the simulated annealing algorithm is combined with discrete particle swarm. We use SA to enhance the results of local best positions instead of current positions. The experimental results show that our hybrid discrete particle swarm algorithm is effective and efficient to solve open-shop problems. © Springer-Verlag Berlin Heidelberg 2006.

关键词： open systems

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The vehicle audio encryption system based on scrambling thought

The vehicle audio encryption system based on scrambling thou...

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2018 International Conference on Network, Communication, Computer engineering (NCCE 2018)

作者： Peibin Wu Jindong Zhang Yang Liu Dongqi Han Yiding Sun College of Computer Science and Technology Jilin University Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University State Key Laboratory of Automobile Simulation and Control Jilin University

ISBN: (纸本)9781510871076

In this paper,the vehicle audio encryption system based on scrambling thought and applied to vehicle-mounted media is *** pseudo random number is generated each time it is encrypted,and it is substituted into the Fibonacci transform to generate a key with variable *** audio data is divided by the number of pseudo random numbers into the Fibonacci sequence,and is encrypted in permutation *** Fibonacci transform has symmetry and creates an audio file that cannot be parsed and played after the permutation *** the security analysis of encryption algorithm,the audio files of different length are *** key space,anti-attack ability,key sensitivity and other indicators are tested to achieve good *** addition,this algorithm is simple in calculation and can realize the ideal real-time performance in practical application.

关键词： vehicle media audio encryption scrambling thought security analysis

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RiWalk: Fast structural node embedding via role identification

arXiv

引用

arXiv 2019年

作者： Ma, Xuewei Qin, Geng Qiu, Zhiyang Zheng, Mingxin Wang, Zhe College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China

Nodes performing different functions in a network have different roles, and these roles can be gleaned from the structure of the network. Learning latent representations for the roles of nodes helps to understand the network and to transfer knowledge across networks. However, most existing structural embedding approaches suffer from high computation and space cost or rely on heuristic feature engineering. Here we propose RiWalk, a flexible paradigm for learning structural node representations. It decouples the structural embedding problem into a role identification procedure and a network embedding procedure. Through role identification, rooted kernels with structural dependencies kept are built to better integrate network embedding methods. To demonstrate the effectiveness of RiWalk, we develop two different role identification methods named RiWalk-SP and RiWalk-WL respectively and employ random walk based network embedding methods. Experiments on within-network classification tasks show that our proposed algorithms achieve comparable performance with other baselines while being an order of magnitude more efficient. Besides, we also conduct across-network role classification tasks. The results show potential of structural embeddings in transfer learning. RiWalk is also scalable, making it capable of capturing structural roles in massive networks. Copyright © 2019, The Authors. All rights reserved.

关键词： Network embeddings

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Live Video Streaming Authentication for Vehicle Multimedia based on Merkle Tree

Live Video Streaming Authentication for Vehicle Multimedia b...

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2018 International Conference on Network, Communication, Computer engineering (NCCE 2018)

作者： Dongqi Han Jindong Zhang Yang Liu Peibin Wu Yiding Sun College of Computer Science and Technology Jilin University Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University State Key Laboratory of Automobile Simulation and Control Jilin University

ISBN: (纸本)9781510871076

Merkle tree has been widely applied in the field of P2 P *** this paper,an efficient authentication scheme is proposed in the integrity check module for vehicle live video streaming system by optimizing the construction and verification of the Merkle *** can create and check nodes at the same time in a shorter time without trusted *** the same time,in order to prevent the oversensitivity of hash algorithm,this paper uses the perceptual hash algorithm to generate the hash value of nodes in the Merkle *** performance evaluation and analysis,our algorithm can build a Merkle tree with 2 N-1 nodes only after the N+log2 N times hash *** has better time performance than the traditional tree,whether receiving attacks or *** it is proved that the scheme is secure under the condition that the vehicle live video streaming system is safe.

关键词： vehicle streaming media system Merkle tree P2P-streaming integrity authentication perceptual hash algorithm

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