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International Workshop on knowledge Discovery and Data Mining (WKDD)

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

ISBN: (纸本)9781424453979

In this paper, we present a novel deterministic heuristic and a new genetic algorithm to solve the problem of optimal triangulation of Bayesian networks. The heuristic, named MinFillWeight, aims to select variables minimizing the multiplication of the weights on nodes of fill-in edges. The genetic algorithm, named GA-MFW, uses a new rank-reserving crossover operator and a 2-fold mutation mechanism utilizing the MinFillWeight heuristic. Experiments on representative benchmark show that the deterministic heuristic and the stochastic algorithm have good performance and stability to various problems.

关键词： Stochastic processes Bayesian methods Tree graphs Ethics Genetic algorithms Inference algorithms Genetic mutations Stability Ant colony optimization Data mining

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A Driving Area Detection Algorithm Based on Swin Transformer 5

A Driving Area Detection Algorithm Based on Swin Transformer

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5th International Conference on Frontiers Technology of Information and Computer, ICFTIC 2023

作者： Liu, Shuang Li, Ying Jilin University College of Computer Science and Technology Jilin Changchun130012 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin Changchun130012 China

ISBN: (纸本)9798350309034

The detection of drivable areas holds immense significance within the perception system of autonomous vehicles. This capability enables intelligent vehicles to gain a comprehensive understanding of the current road conditions and effectively identify safe areas suitable for safe navigation. Convolutional Neural Networks (CNNs) have achieved significant progress in the application of drivable areas detection, particularly in semantic segmentation. However, most existing CNN-based methods for drivable area detection only consider information within a local neighborhood, thus failing to effectively capture long-range dependencies. To tackle this concern, we propose a novel U-shaped network based on the Swin Transformer. The Swin Transformer block was employed as the fundamental element of both the encoder and decoder. Additionally, an upsampling layer was introduced in the decoder to effectively restore the resolution of the feature map. We evaluate our approach on the Cityscapes dataset, a publicly available benchmark for autonomous driving. Experimental results verify the exceptional performance of our proposed method, achieving an impressive mean Intersection over Union (mIoU) score of 91.61%. Our method outperforms existing state-of-the-art techniques in road segmentation detection, further demonstrating its superiority. © 2023 IEEE.

关键词： CNNS Driving Area Detection Semantic segmentation Swin Transformer

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Spatiotemporal Transformer for Data Inference and Long Prediction in Sparse Mobile CrowdSensing 42

Spatiotemporal Transformer for Data Inference and Long Predi...

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42nd IEEE International Conference on Computer Communications, INFOCOM 2023

作者： Wang, En Liu, Weiting Liu, Wenbin Xiang, Chaocan Yang, Bo Yang, Yongjian Jilin University College of Computer Science and Technology China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China Chongqing University College of Computer Science China

ISBN: (纸本)9798350334142

Mobile CrowdSensing (MCS) is a data sensing paradigm that recruits users carrying mobile terminals to collect data. As its variant, Sparse MCS has been further proposed for large-scale and fine-grained sensing task with the advantage of collecting only a few data to infer unsensed data. However, in many real-world scenarios, such as early prevention of epidemic, people are interested in not only the data at the current, but also in the future or even long-term future, and the latter may be more important. Long-term prediction not only reduces sensing cost, but also identifies trends or other characteristics of the data. In this paper, we propose a spatiotemporal model based on Transformer to infer and predict the data with sparse sensed data by utilizing spatiotemporal relationships. We design a spatiotemporal feature embedding to embed the prior spatiotemporal information of sensing map into the model to guide model learning. Moreover, we also design a novel multi-head spatiotemporal attention mechanism to dynamically capture spatiotemporal relationships among data. Extensive experiments have been conducted on three types of typical urban sensing tasks, which verify the effectiveness of our proposed algorithms in improving the inference and long-term prediction accuracy with the sparse sensed data. © 2023 IEEE.

关键词： Forecasting

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Design and Implementation of Judge System for Old Man Fall Down

Design and Implementation of Judge System for Old Man Fall D...

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2017 International Conference on Mechanical,Electronic,Control and Automation engineering(MECAE 2017)

作者： Chao Sun Jindong Zhang Jiajia Lin Rui Song Xuan Zang Xiaohan Liu College of Software Jilin University College of Computer Science and Technology Jilin University Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University

ISBN: (纸本)9781510838451

This paper taking into account the universal use of Android phones, designs and implements judgments and notices the old man falling cell phone App. By calling the phones accelerometer, gyroscope collection campaigns after processing according to human body dynamics model to determine the specific state of the elderly, and get the specific latitude and longitude via GPS;Cell phone judge after the old man falls can send text messages to bound phone number in advance and inform the GPS to obtain location coordinates, after the fall in the elderly will be able to make the necessary relief in a timely manner.

关键词： APP Fall down Dynamic models GPS

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A Method of Identifying Thunderstorm Clouds in Satellite Cloud Image Based on Clustering

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Computers, Materials & Continua 2018年第12期57卷 549-570页

作者： Lili He Dantong Ouyang Meng Wang Hongtao Bai Qianlong Yang Yaqing Liu Yu Jiang College of Computer Science and Technology Jilin UniversityChangchun130012China Key Laboratory of Symbolic Computation and Knowledge Engineering Jilin UniversityChangchun130012China School of Information Science&Technology Dalian Maritime UniversityDalian116026China School of Informatics University of EdinburghEdinburghEH89ABUK

In this paper,the clustering analysis is applied to the satellite image segmentation,and a cloud-based thunderstorm cloud recognition method is proposed in combination with the strong cloud computing *** method firstly adopts the fuzzy C-means clustering(FCM)to obtain the satellite cloud image ***,in the cloud image,we dispose the‘high-density connected’pixels in the same cloud clusters and the‘low-density connected’pixels in different cloud ***,we apply the DBSCAN algorithm to the cloud image obtained in the first step to realize cloud cluster ***,using the method of spectral threshold recognition and texture feature recognition in the steps of cloud clusters,thunderstorm cloud clusters are quickly and accurately *** experimental results show that cluster analysis has high research and application value in the segmentation processing of meteorological satellite cloud images.

关键词： Cloud computing cluster analysis FCM DBSCAN thunderstorm clouds satellite cloud image

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Detection of Glue Spray on Bottom of Vehicle Based on Machine Vision

Detection of Glue Spray on Bottom of Vehicle Based on Machin...

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

作者： Dongyang Liu Jindong Zhang Xiaofeng Zhang Xiaoyu Ma Yuxin Kou College of Computer Science and Technology Jilin University Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University College of Software Jilin University

ISBN: (纸本)9781510871076

With the continuous development of machine vision,intelligent detection of bottom spray glue has gradually become a *** this paper,we proposed an algorithm of detection the glue spray on bottom of vehicle based on machine *** sampling and detecting the spray interest area,the algorithm can obtain its features which were used for intelligent identification of the spray interest area,and then carry out the pseudo target and detect whether the detection area is complete or *** can test the vehicles on pipeline in real *** the average,each frame's processing time is less than 150 *** there is an unqualified area,it will alarm and inform the operators of the wrong *** algorithm can improve the quality of the vehicle and save human resources and identify the spray interest area with high *** research directions in the future is to accelerate the speed to meet the practical application requirements,and to solve the intelligent identification of the spray interest area which has the same color with the vehicle body.

关键词： Glue spray Machine vision intelligent detection

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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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ADAPTIVE DEPTH GRAPH ATTENTION NETWORKS

arXiv

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arXiv 2023年

作者： Zhou, Jingbo Du, Yixuan Zhang, Ruqiong Zhang, Rui Key Laboratory of Symbolic Computation and Knowledge Engineering of MOE Jilin University China

As one of the most popular GNN architectures, the graph attention networks (GAT) is considered the most advanced learning architecture for graph representation and has been widely used in various graph mining tasks with impressive results. However, since GAT was proposed, none of the existing studies have provided systematic insight into the relationship between the performance of GAT and the number of layers, which is a critical issue in guiding model performance improvement. In this paper, we perform a systematic experimental evaluation and based on the experimental results, we find two important facts: (1) the main factor limiting the accuracy of the GAT model as the number of layers increases is the oversquashing phenomenon;(2) among the previous improvements applied to the GNN model, only the residual connection can significantly improve the GAT model performance. We combine these two important findings to provide a theoretical explanation that it is the residual connection that mitigates the loss of original feature information due to oversquashing and thus improves the deep GAT model performance. This provides empirical insights and guidelines for researchers to design the GAT variant model with appropriate depth and well performance. To demonstrate the effectiveness of our proposed guidelines, we propose a GAT variant model-ADGAT that adaptively selects the number of layers based on the sparsity of the graph, and experimentally demonstrate that the effectiveness of our model is significantly improved over the original GAT. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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RiWalk: Fast Structural Node Embedding via Role Identification

RiWalk: Fast Structural Node Embedding via Role Identificati...

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IEEE International Conference on Data Mining (ICDM)

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

关键词： Kernel Task analysis knowledge engineering Machine learning Bridges Proteins Network topology

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Optimization strategy of top-down join enumeration on modern multi-core CPUs

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Journal of Computers 2011年第10期6卷 2004-2012页

作者： Zuo, Wanli Chen, Yongheng He, Fenglin Chen, Kerui Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University Changchun China College of Computer Science and Technology Jilin University Changchun China

Most contemporary database systems query optimizers exploit System-R's bottom-up dynamic programming method (DP) to find the optimal query execution plan (QEP) without evaluating redundant subplans. The distinguished exceptions are Volcano/Cascades using transforms to generate new plans according to a topdown approach. As recent research has revealed, bottom-up dynamic programming can improve performance with respect to the shape of the join graph and parallelism. However top-down join enumeration dynamic programming method can derive upper bounds for the costs of the plans it generates which is not available to typical bottom-up DP method. In this paper, we propose a comprehensive and practical framework for parallelizing top-down dynamic programming query optimization with complex non-inner join in the multi-core processor architecture, referred as PTDhyp. We have implemented such a search strategy and experimental results show that can improve optimization time effective compared to known existing algorithms. © 2011 ACADEMY PUBLISHER.

关键词： Dynamic programming

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