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Investigation and Ecological Evaluation of Plant Resources on Campus of Jiamusi University

Investigation and Ecological Evaluation of Plant Resources o...

2019 International Conference on Building Energy Conservation, Thermal Safety and Environmental Pollution Control, ICBTE 2019

作者： Liu, Haoru Yang, Tianqi Li, Shengju Gu, Ye Li, Lan College of Science Jiamusi University Jiamusi City Heilongjiang Province154007 China School of Data Science and Software Engineering Wuzhou University Wuzhou City Guangxi Province543000 China

The purpose of the systematic investigation of plant resources in Jiamusi University campus is to understand the composition, community characteristics and ecological distribution of plant resources in Jiamusi University, and to make rational use of them. In the process of investigation, the methods of actual step-by-step search and data search are adopted to carry out ecological evaluation. According to statistics, there are 113 kinds of plants on Jiamusi University campus, of which 93 are woody plants and 18 are herbaceous plants. Its growth environment is suitable for forest margin, undergrowth, hillside, and shrub and so on. Its main uses are greening, shading, dust prevention and noise reduction. It has high value. It is hoped that the research will provide a basis for the future construction of ecological civilization on campus. © 2019 The Authors, published by EDP sciences.

关键词： Noise abatement

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A zero-shot based fingerprint presentation attack detection system

arXiv

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arXiv 2020年

作者： Liu, Haozhe Zhang, Wentian Liu, Guojie Liu, Feng National Engineering Laboratory for Big Data System Computing Technology Guangdong Key Laboratory of IntelligentInformation Processing College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

With the development of presentation attacks, Automated Fingerprint Recognition Systems(AFRSs) are vulnerable to presentation attack. Thus, numerous methods of presentation attack detection(PAD) have been proposed to ensure the normal utilization of AFRS. However, the demand of large-scale presentation attack images and the low-level generalization ability always astrict existing PAD methods' actual performances. Therefore, we propose a novel Zero-Shot Presentation Attack Detection Model to guarantee the generalization of the PAD model. The proposed ZSPAD-Model based on generative model does not utilize any negative samples in the process of establishment, which ensures the robustness for various types or materials based presentation attack. Different from other auto-encoder based model, the Fine-grained Map architecture is proposed to refine the reconstruction error of the auto-encoder networks and a task-specific gaussian model is utilized to improve the quality of clustering. Meanwhile, in order to improve the performance of the proposed model, 9 confidence scores are discussed in this article. Experimental results showed that the ZSPAD-Model is the state of the art for ZSPAD, and the MS-Score is the best confidence score. Compared with existing methods, the proposed ZSPAD-Model performs better than the feature-based method and under the multi-shot setting, the proposed method overperforms the learning based method with little training data. When large training data is available, their results are similar. Copyright © 2020, The Authors. All rights reserved.

关键词： Palmprint recognition

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The DUDFTO Attack: Towards Down-to-UP Timeout Probing and Dynamically Flow Table Overflowing in SDN

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IEEE Transactions on Network and Service Management 2025年

作者： Li, Jiasong Cui, Yunhe Chen, Yi Shen, Guowei Guo, Chun Qian, Qing Guizhou University State Key Laboratory of Public Big Data Engineering Research Center of Text Computing and Cognitive Intelligence Ministry of Education College of Computer Science and Technology Guizhou Provincial Characteristic Key Laboratory of Software Engineering and Information Security Guiyang550000 China Guizhou University of Finance and Economics School of Information Guiyang550000 China

As a new network structure, the decoupling of the control plane and forwarding plane makes software-Defined Networking (SDN) widely used in large-scale network scenarios. However, the decoupling network architecture also brings new vulnerabilities. The flow table overflow attack is an attack strategy that can overwhelm SDN switches. Nevertheless, the existing flow table overflow attacks may fail in probing timeouts and match fields of flow entries, due to link failure, measurement of the round-trip time (RTT) of different packets, interference of hard-timeout and idle-timeout. Meanwhile, the stealthiness of the existing attacks may also reduce, as these attacks use fixed attack rate. To improve the timeout probing accuracy and the stealthiness of attack, a new flow table overflow attack strategy, DUDFTO, is proposed to accurately probe timeout settings and match fields, then stealthily overflow SDN flow tables. Firstly, it probes the match fields by measuring the one-sided transmission delay of the packets. After that, DUDFTO designs a down-to-up feedback-based timeout probing algorithm to eliminate the issues caused by high RTT, link failure, interference between hard-timeout and idle-timeout. Then, DUDFTO designs a dynamic attack packets sending algorithm to improve its stealthiness. Finally, DUDFTO probes the flow table state to stop sending new attack packets. The evaluation results demonstrate that DUDFTO outperforms the existing attacks in terms of match fields probing ability, timeout probing relative error, number of packet_in and flow_mod messages generated by the attack, rate distribution of packet_in and flow_mod messages generated during the attack, and number of detected attack packets. © 2004-2012 IEEE.

关键词： flow table overflow attack information probing SDN switchs

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Correction to: Recent advances in multiple criteria decision making techniques

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International Journal of Machine Learning and Cybernetics 2021年第2期13卷 565-565页

作者： He, Yulin Wang, Xizhao Huang, Joshua Zhexue Big Data Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China

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Dual-domain decoupled fusion network for semantic segmentation of remote sensing images

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Information Fusion 2025年 124卷

作者： Li, Xin Xu, Feng Zhang, Jue Yu, Anzhu Lyu, Xin Gao, Hongmin Zhou, Jun College of Computer Science and Software Engineering Hohai University Nanjing China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China School of Engineering and Built Environment Griffith University Nathan Australia School of Surveying and Mapping Information Engineering University Zhengzhou China School of Information and Communication Technology Griffith University Nathan Australia

Semantic segmentation of remote sensing images (RSIs) is a challenging task due to the complexity of spatial structures, diverse object scales, and heterogeneous land-cover patterns. Traditional approaches often struggle to effectively balance fine-grained boundary details and global contextual understanding, especially for high-resolution images. In this paper, we propose DDFNet, a novel dual-domain decoupled fusion network, to address these challenges. DDFNet integrates spatial and frequency domain features through a dynamic decoupling and fusion strategy. Specifically, we introduce a Dual-Domain Decoupled Feature Fusion (DDFF) module that selectively combines high- and low-frequency components from both domains, enabling the model to capture local textures and global semantics. To further enhance segmentation accuracy, we design a High-Order Geometric Prior Generation (HGPG) module, which utilizes gradient and curvature information to improve boundary precision and maintain geometric consistency. Extensive experiments on three benchmark datasets — ISPRS Vaihingen, ISPRS Potsdam, and LoveDA — demonstrate that DDFNet achieves state-of-the-art performance. Ablation studies validate the contributions of the DDFF and HGPG modules, and efficiency analysis shows DDFNet's strong adaptability to different computational constraints. © 2025 Elsevier B.V.

关键词： Attention mechanism Feature fusion Remote sensing images Semantic segmentation

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A case study of MapReduce-based expressway traffic data analysis and service system

A case study of MapReduce-based expressway traffic data anal...

引用

International Journal of Internet Manufacturing and Services

作者： Liu, Jia Hong, Zhilong Mo, Tong Ding, Weilong Zhang, Jian Li, Weiping Li, Haochen Henan Expressway Network Monitoring Charge Communication Service Co. Ltd. Henan China School of Software and Microelectronics Peking University Beijing China Data Engineering Institute North China University of Technology Beijing China School of Economics and Management Beijing Information Science and Technology University Beijing China College of Science China Agricultural University Beijing China

The scale of expressway information networking is constantly expanding. Currently the existing analysis system is still built on the relational database. Traffic data produced by the system has reached a data volume of 3 million items monthly. The performance requirements, including high concurrency, massive throughput, visualisation and scalability, are difficult to be satisfied. The expressway traffic data analysis system (ETDAS) is designed to meet the needs of the collection, analysis and visualisation of increasing expressway traffic data by means of the distributed frameworks. The new system is expected to help regulate the road network traffic flow, reduce traffic congestion, and provide analytical support for the optimisation strategy of road network. ETDAS has been deployed online. © 2020 Inderscience Enterprises Ltd.

关键词： Traffic congestion data handling Information analysis Motor transportation Roads and streets Visualization Analysis system Distributed framework High concurrencies Information networking Performance requirements Relational database Road network traffic Traffic data analysis

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CheXMed: A Multimodal Algorithm to Improve Pneumonia Detection in the Elderly

SSRN

引用

SSRN 2023年

作者： Hao, Ren Jing, Fengshi Chen, Zhurong Liu, Le Zhou, Jiandong Jing, Ran Lian, Wanmin Tian, Junzhang Xu, Zhongzhi Zhang, Qingpeng Cheng, Weibin Institute for Healthcare Artificial Intelligence Guangdong Second Provincial General Hospital Guangzhou510317 China UNC Project-China UNC Global School of Medicine University of North Carolina Chapel HillNC27599 United States College of Computer Science and Software Engineering Shenzhen University Shenzhen518061 China College of Foreign Languages Jishou University Zhangjiajie427000 China Nuffield Department of Medicine University of Oxford OxfordOX3 9DU United Kingdom Information Department Guangdong Second Provincial General Hospital Guangzhou510317 China School of Public Health Sun Yat-sen University Guangzhou510080 China School of Data Science City University of Hong Kong Hong Kong

Objectives: Pneumonia can be a deadly illness for particular populations, one of which is older adults. While studies have successfully trained artificial intelligent assisted diagnostic tools to detect pneumonia using chest X-ray images, they were targeted to the general population without stratification on age ***: This study investigated the performance disparities between geriatric and younger patients when using chest X-ray images to detect pneumonia, and developed and tested a multimodal algorithm that incorporates clinical notes together with image data to improve pneumonia detection performance for older *** and Methods: Three most used pneumonia diagnostic models, CheXNet, DenseNet121, and ResNet18, were evaluated to test their performance in older persons. Electronic medical records and frontal-view chest X-ray images of 2,572 inpatients were collected in a local hospital data to train and test a multi-input deep neural network framework named CheXMed, for pneumonia detection among older patients. Accuracy, precision, recall, F1-score, and AUC were used for model performance ***: CheXNet, DenseNet121, and ResNet18 achieved 9.5%, 6.0%, and 6.9% of higher AUC in the younger age group than in the elderly group, respectively. The proposed novel model, CheXMed, outperforms CheXNet by 15.3% in accuracy, 9.0% in precision, 38.3% in recall, and 24.0% in ***: Diagnostic models achieving good performance for young adults or the general population do not guarantee that they are useful for the elderly. Chest X-ray images combined with clinical information significantly improve pneumonia detection performance among elderly patients who are more vulnerable while neglected in general models. © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Novel Truncated-rank Graph-structured and Tree-guided Sparse Linear Mixed Models for Variable Selection on Genome-wide Association Studies

Novel Truncated-rank Graph-structured and Tree-guided Sparse...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiang Liu Xueling Liu Jing Diao Mengyao Zheng Jihe Li Yongyi Xie Kang Lai Xiao Geng Yijun Song Linshan Jiang School of Computing National University of Singapore Group Technology Office National University Health System Department of Preventive Dentistry Peking University School and Hospital of Stomatology National Center for Stomatology & National Clinical Research Center for Oral Diseases Harvard T.H. Chan School of Public Health Harvard University School of Software and Microelectronics Peking University Shanghai Skin Disease Hospital Institute of Dermatology School of Medicine Tongji University School of Mechanical & Aerospace Engineering Nanyang Technological University Orthopaedic Department Peking University Third Hospital Engineering Research Center of Bone and Joint Precision Medicine Ministry of Education Dongfang College Zhejiang University of Finance & Economics Institute of Data Science National University of Singapore

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Variable selection for genome-wide association studies is a key focus for bioinformatics researchers in high-performance computing. The rapid growth of biological and biomedical data demands has led to high-dimensional, heterogeneous datasets characterized by non-i.i.d. properties and numerous response variables, often resulting in false negatives or positives in recovered results. Traditional methods, when nal̈ively applied, yield suboptimal performance due to confounding factors. To account for the complex interdependencies in heterogeneous data and enhance the practical outcomes of genome-wide association studies, we introduce two methods, TGsLMM and TTsLMM, which balance effects between response and explanatory variables for subpopulation inference. Our unified framework performs sparse variable selection using graph-structured or tree-guided structures in a low-rank linear mixed model. Additionally, we extend our approach to high-dimensional datasets and adaptively select the covariance structure for genomic data. Extensive experiments on synthetic and three real-world datasets emphasize the robustness and effectiveness of our proposed methods, achieving the highest ROC area compared to baselines and superior results for future potential.

关键词： Adaptation models Input variables Biological system modeling High performance computing Noise Genomics Medical services data models Robustness Bioinformatics

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Spark-based parallelization of basic local alignment search tool

引用

International Journal Bioautomation 2020年第1期24卷 87-98页

作者： Wang, Hui Li, Leixiao Zhou, Chengdong Lin, Hao Deng, Dan College of Data Science and Application Inner Mongolia University of Technology Hohhot010080 China Inner Mongolia Autonomous Region Engineering and Technology Research Center of Big Data Based Software Service Inner Mongolia University of Technology Hohhot010080 China

Sequence alignment is a key link of bioinformatics analysis. The basic local alignment search tool (BLAST) is a popular sequence alignment algorithm with high accuracy. However, the BLAST is inefficient in comparing and analyzing a massive amount of gene sequencing data. To solve the problem, this paper designs a distributed parallel BLAST method called SparkBLAST, based on the big data technique Spark. Under the in-memory computing framework Spark, SparkBLAST identifies the task of sequence alignment, divides the sequence dataset, and compares the sequence data. The Apache Hadoop YARN was adopted to task scheduling and resource allocation. Finally, the SparkBLAST was compared with standalone BLAST through experiments. The results show that SparkBLAST realized the speedup ratio of 3.95, without sacrificing the accuracy. In other words, SparkBLAST greatly outshines the standalone BLAST in calculation efficiency. The research findings provide bioinformatics researchers a highly efficient tool for sequence alignment. © 2020 by the authors.

关键词： Computer software

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Factor space is the adaptive and deepening theory of fuzzy sets

Factor space is the adaptive and deepening theory of fuzzy s...

引用

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Haitao Liu Runjun Wan Shanshan Xue Tiantian Wang Sizong Guo Jing He Institute of Intelligence Engineering and Mathematics College of Science Liaoning Technical University Fuxin China Dept. of Data Science and knowledge Engineering University of Maastricht Netherlands School of software and electrical engineering Swinburne University of Technology Hawthorn Australia

ISBN: (数字)9781728169323

ISBN: (纸本)9781728169330

In recent years, the factor space theory has been promoted gradually as math foundation for mechanistic artificial intelligence theory. The theory was put forward in 1982 by Prof. P Z Wang, he took the fundamental space Ω in probability and the universe U of fuzzy sets both as factor spaces, but put Ω in the sky 2 U =P(U). The established fuzzy shadow theory says that the membership function on the ground is the coverage of random set in the sky, and had proved the Existence and Uniqueness Theorem on the correspondence between earth and heaven. This theory points out that the adaptive platform of intelligent description and subjective measurement is the factor space; and the core transform in between different levels is the power mapping (falling shadow). This is the mathematical secret of artificial intelligence, but also the direction of further improvement of fuzzy sets and systems.

关键词： Probability distribution Fuzzy sets Random variables Shape Artificial intelligence Aerospace electronics

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