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Global stability of the periodic solution of the three level food chain model with extinction of top predator

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arXiv 2021年

作者： Cheng, Kaijen You, Hongming Yang, Ting-Hui School of Mathematics and Statistics Zhaoqing University Guangdong Zhaoqing526061 China College of Mathematics and Computer Science Quanzhou Normal University Quanzhou362000 China Department of Mathematics Tamkang University No. 151 Yingzhuan Road Tamsui Dist. New Taipei City25137 Taiwan Fujian Provincial Key Laboratory of Data-Intensive Computing Key Laboratory of Intelligent Computing and Information Processing School of Mathematics and Computer Science Quanzhou Normal University Quanzhou China

In this work, we revisit the classical Holling type II three species food chain model from a different viewpoint. Two critical parameters λ1 and λ2 dependent on all parameters are defined. The existence and local stabilities of all equilibria can be reformulated by λ1 and λ2, and the complete classifications of parameters and its corresponding dynamics are given. Moreover, with the extinction of top-predator, there is an invariant two dimensional subsystem containing the prey and the intermediate predator. We prove the global stability of the boundary equilibrium in 3+ by differential inequality as well as Butler-McGehee lemma if it is stable. Alternatively, there is a unique limit cycle when the boundary equilibrium lost its stability, and we also show the global stability of the limit cycle in 3+ by differential inequality and computing the Floquet Multipliers. Finally, some interesting numerical simulations, the chaotic and the bi-stability phenomena, are presented numerically. A brief discussion and biological implications are also *** Codes 92D25 Copyright © 2021, The Authors. All rights reserved.

关键词： Stability

Does Thermal Really Always Matter for RGB-T Salient Object Detection?

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arXiv 2022年

作者： Cong, Runmin Zhang, Kepu Zhang, Chen Zheng, Feng Zhao, Yao Huang, Qingming Kwong, Sam The Institute of Information Science Beijing Jiaotong University Beijing100044 China The Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China The Department of Computer Science City University of Hong Kong China The Department of Computer Science and Technology Southern University of Science and Technology Shenzhen518055 China The Research Institute of Trustworthy Autonomous Systems Shenzhen518055 China The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Peng Cheng Laboratory Shenzhen518055 China The City University of Hong Kong Shenzhen Research Institute Shenzhen51800 China

In recent years, RGB-T salient object detection (SOD) has attracted continuous attention, which makes it possible to identify salient objects in environments such as low light by introducing thermal image. However, most of the existing RGBT SOD models focus on how to perform cross-modality feature fusion, ignoring whether thermal image is really always matter in SOD task. Starting from the definition and nature of this task, this paper rethinks the connotation of thermal modality, and proposes a network named TNet to solve the RGB-T SOD task. In this paper, we introduce a global illumination estimation module to predict the global illuminance score of the image, so as to regulate the role played by the two modalities. In addition, considering the role of thermal modality, we set up different cross-modality interaction mechanisms in the encoding phase and the decoding phase. On the one hand, we introduce a semantic constraint provider to enrich the semantics of thermal images in the encoding phase, which makes thermal modality more suitable for the SOD task. On the other hand, we introduce a two-stage localization and complementation module in the decoding phase to transfer object localization cue and internal integrity cue in thermal features to the RGB modality. Extensive experiments on three datasets show that the proposed TNet achieves competitive performance compared with 20 state-of-the-art methods. The code and results can be found from the link of https://***/proj_***. © 2022, CC BY-NC-SA.

关键词： Semantics

Object tracking by the least spatiotemporal searches

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arXiv 2020年

作者： Yu, Zhiyong Han, Lei Chen, Chao Guo, Wenzhong Yu, Zhiwen College of Mathematics and Computer Sciences Fuzhou University Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350108 China School of Computer Science Northwestern Polytechnical University Xi'an710072 China School of Computer Science Chongqing University Chongqing400044 China

Tracking a suspicious car or a person in a city efficiently is crucial in urban safety management. But how can we complete the task with the minimal number of spatiotemporal searches when massive camera records are involved? To this end, this study proposes a strategy named intermediate searching at heuristic moments (IHMs). At each step, we determine which moment is the best one for the search based on a heuristic indicator. Then, at that moment, locations are searched one by one in descending order of predicted appearing probabilities until a search hit is obtained. We iterate this step until we derive the object's current location. Five searching strategies are compared via experiments. Among these strategies, the IHMs strategy is validated as the most efficient. IHMs can save up to 1/3 of the total cost. This result provides evidence that "searching at intermediate moments can save cost. Copyright © 2020, The Authors. All rights reserved.

关键词： Heuristic algorithms

Exploring multi-loss function based attention framework for vehicle re-identification 10

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Exploring multi-loss function based attention framework for ...

10th International Conference on information Technology in Medicine and Education, ITME 2019

作者： Liu, Wei Zhang, Yi Luo, Zhiming Li, Shaozi East China Jiaotong University Jiangxi China Cognitive Science Department Xiamen University Fujian China Postdoc Center of Information and Communication Engineering Xiamen University China Key Lab. of Cogn. Computing and Intelligent Information Processing of Fujian Education Institutions Wuyishan China Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering Yi Chang China

ISBN: (纸本)9781728139173

Vehicle Re-identification(Re-ID) refers to the task of matching a specific vehicle across multiple non-overlapping cameras. Precise vehicle Re-ID suffers from two main challenges: (1) different vehicle identities with a similar appearance of the same type have a subtle inter-instance discrepancy, (2) one vehicle usually has large intra-instance differences due to viewpoint and illumination variations. To address these challenges, in this paper, we proposed an Attention-based Framework for Vehicle Re-ID named AFVR, which combines the benefits of both the cross-entropy loss function and the triplet loss function. Experimental results on two popular vehicle Re-ID benchmarks demonstrate the effectiveness of our proposed method, compared with the state of the arts. © 2019 IEEE.

关键词： Vehicles

Adaptively Extracting Structured Data from Web Pages

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Adaptively Extracting Structured Data from Web Pages

IEEE International Conference on Big Data and Cloud computing (BdCloud)

作者： Yingnan Guo Jiajun Zhang Xing Chen College of Mathematics and Computer Science Fuzhou University Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Web pages contain a large amount of valuable information and resources, meanwhile may update at any time. However, the current Web-data extraction algorithms are generally targeted at specific web page structure. When web pages update, the problem which is caused by the changes of web pages may be encountered, leading to the inability to extract web page information or wrong information. In order to solve this problem, this paper proposes a new method to extract the feature values of each area in the web page through page rendering, and then combine the DOM tree structure of the page, semantic similarity and other information, so that it can still extract the target data correctly after the structure of the web page changes.

关键词： Data mining Web pages Feature extraction Layout Indexes Distributed processing Big Data

A Genetic Algorithm Based Piecewise Linear Representation of Time Series 14

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A Genetic Algorithm Based Piecewise Linear Representation of...

14th IEEE International Conference on intelligent Systems and Knowledge Engineering, ISKE 2019

作者： Yang, Xiyang Zhai, Changxin Li, Fang Liu, Longshu Zhang, Youhua Quanzhou Normal University Key Laboratory of Intelligent Computing and Information Processing of Fujian Province Quanzhou China Quanzhou Normal University School of Mathematics and Computer Science Quanzhou China Beijing Normal University School of Mathematical Science Beijing China

ISBN: (纸本)9781728123486

Line Segment Representation (LSR) refers to represents a time series by a few of line segments, such that the original time series and the piecewise line segment series have shapes as similar as possible. Because of its simple expression, LSR based time series are often easier to be understood and computed for some time series datamining tasks than the original raw data. Two kinds of continuous LSR methods, namely, 11 trend filtering and mix-integer programming (MILP) method, are discussed in this paper. To overcome the poor representation ability of l1 trend filtering, and the high computational complexity of MILP, this paper proposes a hybrid method combining GA and linear programming (GA-LP) to find the optimal LSR time series efficiently. In GA-LP, locations of the breakpoints of the piecewise linear segment are fixed by GA, and values on these locations are fixed by a LP method. Numerical experiments reveal that GA-LP can reduce representation error by comparisons with l1 trend filtering and MILP method, and its computing time is much less than that of MILP. © 2019 IEEE.

关键词： Time series

Fair and autonomous sharing of federate learning models in mobile Internet of Things

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arXiv 2020年

作者： Hao, Xiaohan Ren, Wei Xiong, Ruoting Zheng, Xianghan Zhu, Tianqing Xiong, Neal N. School of Computer Science China University of Geoscience Wuhan China Guangxi Key Laboratory of Cryptography and Information Security Guilin541004 China Key Laboratory of Network Assessment Technology CAS Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Mingbyte Technology QingDao China School of Software University of Technology Sydney UltimoNSW2007 Australia Dept. of Mathematics and Computer Science Northeastern State University United States

Federate learning can conduct machine learning as well as protect the privacy of self-owned training data on corresponding ends, instead of having to upload to a central trusted data aggregation server. In mobile scenarios, a centralized trusted server may not be existing, and even though it exists, the delay will not be manageable, e.g., smart driving cars. Thus, mobile federate learning at the edge with privacy-awareness is attracted more and more attentions. It then imposes a problem - after data are trained on a mobile terminal to obtain a learned model, how to share the model parameters among others to create more accurate and robust accumulative final model. This kind of model sharing confronts several challenges, e.g., the sharing must be conducted without a third trusted party (autonomously), and the sharing must be fair as model training (by training data)is valuable. To tackle the above challenges, we propose a smart contract and IPFS (Inter-Planetary File System) based model sharing protocol and algorithms to address the challenges. The proposed protocol does not rely on a trusted third party, where individual-learned models are shared/stored in corresponding ends. Conducted through extensive experiments, three main steps of the proposed protocol are evaluated. The average executive time of the three steps are 0.059s, 0.060s and 0.032s, demonstrating its efficiency. Copyright © 2020, The Authors. All rights reserved.

关键词： Smart contract

Using Apriori Algorithm on Students' Performance Data for Association Rules Mining

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Using Apriori Algorithm on Students' Performance Data for As...

2nd International Seminar on Education Research and Social Science(ISERSS 2019)

作者： Xiaodong Wu Yuzhu Zeng Faculty of Mathematics and Computer Science Quanzhou Normal University Fujian Provincial Key Laboratory of Data Intensive Computing Key Laboratory of Intelligent Computing and Information Processing Fujian Province University

With the development of information technology, many colleges and universities have established student information management system. The long-term operation of the student information management system will generate big data for colleges and universities. Moreover, there exists valuable information in the huge amount of data. Hence, it is necessary to use the data mining method to mine the massive data and get some valuable reference information so as to improve the teaching and management of students. In this paper, the Apriori algorithm is used to mine association rules of 34 courses of 100 students majoring in computer science and technology, so as to find out the correlation between courses and the factors that lead to the high or low grades of courses. R is used to conduct the experiment to discover the association rules, and the association rules are analyzed and discussed. The results of data mining on students' achievements in this work are expected to provide a reference for improving the teaching quality of computer science and technology courses.

关键词： data mining apriori algorithm association rules students' achievement analysis

Research on intelligent recognition and encryption algorithm of network big data image

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IOP Conference Series: Earth and Environmental Science 2021年第4期632卷

作者： Jubao Qu School of mathematics and computer Wuyi University Fujian 354300 China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions (Wuyi University) Wuyi University Wuyishan Fujian 354300 China

In view of the identification and encryption of massive network information in the era of big data, this paper proposes to use intelligent image recognition technology to analyze the required information, then classify it according to the big data clustering method, and use pseudo pixel encryption algorithm for multiple encryption. The simulation results show that the algorithm has high recognition rate, good encryption effect, strong anti-interference ability and good ability to protect network image information.

关键词：