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Temporal and Spatial Succession Law of Abor Diversity in Xiaoxing'an Mountains

IOP Conference Series: Earth and Environmental science 2021年第1期687卷

作者： Jialin Li Jiao Yang XueWei Sun Jincheng Luo Hongbin Qiu Lan Li School of Public Health Jiamusi University Jiamusi City Heilongjiang Province 154007 China School of Data Science and Software Engineering Wuzhou University Wuzhou City Guangxi Province 543000 China

The national forest nature reserve in the Xiaoxing'anling area was studied. Based on the diversity of α and β, three forest types of grassland, birch forest, and larch forest were analyzed. Based on the diversity law of latitude gradient, the nature reserve was discussed in forest type. A difference in a diversity of different forest types was succession changes. A method based on quantitative evaluation of species conservation value was constructed. The community structure and species composition of nature reserves were systematically analyzed, and the conservation value of species and the conservation value of rare and endangered species were scientifically evaluated. The results showed that the Shannon Wiener index and Margalef index of grassland, birch forest, and larch forest showed a decreasing trend with the increase of latitude. Compared with the other four latitudes, the grassland, birch forest and larch forest of Dazhanhe Nature Reserve showed a decreasing trend from the north to south Jaccard index and Sorenson index, and the Cody index showed an increasing trend. The Jaccard index and the Sorenson index have peaks in friendly areas, and the species similarity is high, indicating that the friendly areas may be the interlaced areas of Xiaoxing'anling. The Shannon Wiener index and the Margalef index of the nature reserve from the early stage of succession to the middle and late succession increased continuously, reaching the maximum in the middle and late stages, and began to decline in the later stage of succession, which is consistent with the mid-specie.

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Distilled Transformers with Locally Enhanced Global Representations for Face Forgery Detection

arXiv

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arXiv 2024年

作者： Zhang, Yaning Li, Qiufu Yu, Zitong Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen518129 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Guangdong Shenzhen518060 China School of Computing and Information Technology Great Bay University Guangdong Dongguan523000 China

Face forgery detection (FFD) is devoted to detecting the authenticity of face images. Although current CNN-based works achieve outstanding performance in FFD, they are susceptible to capturing local forgery patterns generated by various manipulation methods. Though transformer-based detectors exhibit improvements in modeling global dependencies, they are not good at exploring local forgery artifacts. Hybrid transformer-based networks are designed to capture local and global manipulated traces, but they tend to suffer from the attention collapse issue as the transformer block goes deeper. Besides, soft labels are rarely available. In this paper, we propose a distilled transformer network (DTN) to capture both rich local and global forgery traces and learn general and common representations for different forgery faces. Specifically, we design a mixture of expert (MoE) module to mine various robust forgery embeddings. Moreover, a locally-enhanced vision transformer (LEVT) module is proposed to learn locally-enhanced global representations. We design a lightweight multi-attention scaling (MAS) module to avoid attention collapse, which can be plugged and played in any transformer-based models with only a slight increase in computational costs. In addition, we propose a deepfake self-distillation (DSD) scheme to provide the model with abundant soft label information. Extensive experiments show that the proposed method surpasses the state of the arts on five deepfake datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Face recognition

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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...

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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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Impact of temperature and relative humidity on the transmission of COVID-19: A modeling study in China and the United States

arXiv

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arXiv 2020年

作者： Wang, Jingyuan Tang, Ke Feng, Kai Lin, Xin Lv, Weifeng Chen, Kun Wang, Fei School of Computer Science and Engineering Beihang University China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University China School of Social Sciences Tsinghua University China State Key Laboratory of Software Development Environment Beihang University China Department of Statistics University of Connecticut United States Center for Population Health University of Connecticut Health Center United States Department of Population Health Sciences Weill Cornell Medical College Cornell University United States

Objectives We aim to assess the impact of temperature and relative humidity on the transmission of COVID-19 across communities after accounting for community-level factors such as demographics, socioeconomic status, and human mobility status. Design A retrospective cross-sectional regression analysis via the Fama-MacBeth procedure is adopted. Setting We use the data for COVID-19 daily symptom-onset cases for 100 Chinese cities and COVID-19 daily confirmed cases for 1,005 U.S. counties. Participants A total of 69,498 cases in China and 740,843 cases in the U.S. are used for calculating the effective reproductive numbers. Primary outcome measures Regression analysis of the impact of temperature and relative humidity on the effective reproductive number (R value). Results Statistically significant negative correlations are found between temperature/relative humidity and the effective reproductive number (R value) in both China and the U.S. Conclusions Higher temperature and higher relative humidity potentially suppress the transmission of COVID-19. Specifically, an increase in temperature by 1 degree Celsius is associated with a reduction in the R value of COVID-19 by 0.026 (95% CI [-0.0395,-0.0125]) in China and by 0.020 (95% CI [-0.0311, -0.0096]) in the U.S.;an increase in relative humidity by 1% is associated with a reduction in the R value by 0.0076 (95% CI [-0.0108,-0.0045]) in China and by 0.0080 (95% CI [-0.0150,-0.0010]) in the U.S. Therefore, the potential impact of temperature/relative humidity on the effective reproductive number alone is not strong enough to stop the pandemic. Copyright © 2020, The Authors. All rights reserved.

关键词： Regression analysis

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Detection of image copy-move forgery using local intensity order pattern

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Tongxin Xuebao/Journal on Communications 2016年 37卷 132-139页

作者： Lin, Jing Huang, Tian-Qiang Lin, Ling-Peng Li, Xiao-Chen School of Mathematics and Computer Science Fujian Normal University Fuzhou350007 China Faculty of Software Fujian Normal University Fuzhou350007 China Fujian Provincial University Engineering Research Center of Big Data Analysis and Application Fujian Normal University Fuzhou350007 China

Copy-move forgery was one of the most simple and common way of image manipulations. To improve the robustness of most existing copy-move forgery detections, a new method based on local intensity order pattern was proposed. First, the LIOP feature descriptors were exacted from the inspected image. Then the angular cosine of feature descriptors were used to measure the similarity, and the stable matching points were found according to the distance ratio threshold of the nearest neighbor point to the second nearest neighbor. Finally, the space distance of the matching points were calculated to remove the false matching points. Extensive experimental results were presented to confirm that the proposed method is not only able to effectively identify and locate the altered area, but also have high accuracy and robust to scaling, rotation, brightness change and some post-processing, such as Gaussian blur, additive white Gaussian noise and JPEG compression. © 2016, Editorial Board of Journal on Communications. All right reserved.

关键词： White noise

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Design of a high-performance full-system instrumentation

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Dongbei Daxue Xuebao/Journal of Northeastern University 2014年 35卷 129-134页

作者： Min, Qing-Hao Zhang, Zheng Wang, Hao-Jun Zhang, Wei-Hua Software School Fudan University Shanghai201203 China Shanghai Key Laboratory of Data Science Fudan University Shanghai201203 China Parallel Processing Institute Fudan University Shanghai201203 China State Key Laboratory of Mathematic Engineering and Advanced Computing PLA Information Engineering University Zhengzhou450001 China

Instrumentation is an important mechanism to collect run-time information of applications for further analysis like bottleneck analysis, hotspot analysis and even bug detection and revision. Currently there are lots of analyzers using instrumentation to collect their source data. However, most of these tools lack of full-system support and thus become inadequate for analyzing multi-core programs as they cannot provide full-system information like scheduling, system calls and inter-process communication, etc. Meanwhile, full-system instrumentation needs to collect OS and hardware information, resulting to performance degradation. In this paper, a high-performance full-system instrumentation method was designed for collecting full-system information as well as application information. The proposed method was implemented inside the QEMU full-system emulator, and 40 MIPS performance on average was achieved after optimization, which was appropriated for normal program analysis. ©, 2014, Northeastern University. All right reserved.

关键词： Full-system High-performance Instrumentation Multi-core

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Effects of Gradient Concentration of Colchicine on Callus Induction and Differentiation ofRose rosea

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IOP Conference Series: Earth and Environmental science 2020年第1期526卷

作者： Jianan Dai Yaosheng Zhang Ye Gu Bin Wen Jinqiu Guan Lan Li Schoolof Science&Life Jiamusi University Jiamusi City Heilongjiang Province 154007 China School of Data Science and Software Engineering Wuzhou University Wuzhou City Guangxi Province 543000 China

To induce polypoid plants ofRosa rugosa tunb, the effect of the interaction between the concentration of colchicine and the treatment time on the callus induction and differentiation of R. rugosa tunb was studied using colchicine as the inducer. The colchicine immersion method was selected, and the callus of red Rosa rugosa tunb was immersed in colchicine of different concentrations for 2h, 4h, and 8h. The best culture medium for inducing callus was as follows: MS+1.0mg/L6-BA+0.1mg/L NAA+9.0 g agar+30.0g sucrose. After cultivation, the callus was dense in texture, light green and obviously granular. After the callus was immersed in colchicine, the combination of different colchicine concentrations and treatment time had an interaction with the callus. Therefore, the best treatment method for polyploid-induced Rosa rugosa tunb polyploid should be higher colchicine combined with shorter processing time. This experiment can provide experimental basis and theoretical basis for cultivating Rosa rugosa tunbpolyploid plant with excellent traits, thereby increasing the content of essential oil in the plant itself.

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Hardening Active Directory Graphs via Evolutionary Diversity Optimization based Policies

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ACM Transactions on Evolutionary Learning and Optimization 1000年

作者： Diksha Goel Max Ward Aneta Neumann Frank Neumann Hung Nguyen Mingyu Guo CSIRO’s Data61 Australia Department of Computer Science and Software Engineering University of Western Australia Australia School of Computer and Mathematical Sciences University of Adelaide Australia

Active Directory (AD) is the default security management system for Windows domain networks. An AD environment can be described as a cyber-attack graph, with nodes representing computers, accounts, etc., and edges indicating existing accesses or known exploits that enable attackers to move from one node to another. This paper explores a Stackelberg game model between one attacker and one defender on an AD attack graph. The attacker’s goal is to maximize their chances of successfully reaching the destination before getting detected. The defender’s aim is to block a constant number of edges to minimize the attacker’s chance of success. The paper shows that the problem is #P-hard and, therefore, intractable to solve exactly. To defend the AD graph from cyber attackers, this paper proposes two defensive approaches. In the first approach, we convert the attacker’s problem to an exponential sized Dynamic Program that is approximated by a Neural Network (NN). Once trained, the NN serves as an efficient fitness function for defender’s Evolutionary Diversity Optimization based defensive policy. The diversity emphasis on the defender’s solution provides a diverse set of training samples, improving the training accuracy of our NN for modeling the attacker. In the second approach, we propose a RL based policy to solve the attacker’s problem and Critic network assisted Evolutionary Diversity Optimization based defensive policy to solve defender’s problem. Experimental results on synthetic AD graphs show that the proposed defensive policies are scalable, highly effective, approximate attacker’s problem accurately, and generate good defensive plans.

关键词： Attack graph active directory reinforcement learning evolutionary diversity optimization

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A Lightweight Experimental Platform for Big data Based on Docker Containers

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Journal of Physics: Conference Series 2020年第1期1437卷

作者： Gu Ruijun Jiangsu Key Laboratory of Data Science and Smart Software Jinling Institute of Technology Nanjing 211169 China School of Computer Engineering Jinling Institute of Technology Nanjing 211169 China

In recent years, many colleges and universities have set up the major of big data. The biggest problem in teaching is that there is no supporting basic experimental environment, and it is difficult to deploy and configure the big data environment at the same time. In addition, the lack of experimental data, experimental teaching plans and experimental manuals in the experimental process makes it difficult to carry out relevant teaching. In order to reduce the cost of laboratory construction and the difficulty of learning big data, a lightweight big data experimental platform was constructed based on virtualized container technology. Through this platform, we can create a big data cluster, provide various suitable experimental environments, focus on the technology itself, and greatly improve the learning efficiency.

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Kurdish Handwritten Character Recognition using Deep Learning Techniques

arXiv

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arXiv 2022年

作者： Ahmed, Rebin M. Rashid, Tarik A. Fattah, Polla Alsadoon, Abeer Bacanin, Nebojsa Mirjalili, Seyedali Vimal, S. Chhabra, Amit IT Department Faculty of Applied Science Tishk International University Erbil Iraq Computer Science and Engineering Department University of Kurdistan Hewler Erbil Iraq Software and Informatics Engineering Salahaddin University-Erbil- Erbil Iraq School of Computing and Mathematics Charles Sturt University Sydney Australia Sydney Australia Singidunum University Belgrade Serbia Centre for Artificial Intelligence Research and Optimisation Torrens University Australia Yonsei Frontier Lab Yonsei University Seoul Korea Republic of Department of Artificial Intelligence and Data Science Ramco Institute of Technology North Venganallur Village Virudhunagar District Tamilnadu Rajapalayam626 117 India Department of Computer Engineering and Technology Guru Nanak Dev University Amritsar India

Handwriting recognition is one of the active and challenging areas of research in the field of image processing and pattern recognition. It has many applications that include: a reading aid for visual impairment, automated reading and processing for bank checks, making any handwritten document searchable, and converting them into structural text form, etc. Moreover, high accuracy rates have been recorded by handwriting recognition systems for English, Chinese Arabic, Persian, and many other languages. Yet there is no such system available for offline Kurdish handwriting recognition. In this paper, an attempt is made to design and develop a model that can recognize handwritten characters for Kurdish alphabets using deep learning techniques. Kurdish (Sorani) contains 34 characters and mainly employs an Arabic\Persian based script with modified alphabets. In this work, a Deep Convolutional Neural Network model is employed that has shown exemplary performance in handwriting recognition systems. Then, a comprehensive dataset was created for handwritten Kurdish characters, which contains more than 40 thousand images. The created dataset has been used for training the Deep Convolutional Neural Network model for classification and recognition tasks. In the proposed system, the experimental results show an acceptable recognition level. The testing results reported a 96% accuracy rate, and training accuracy reported a 97% accuracy rate. From the experimental results, it is clear that the proposed deep learning model is performing well and is comparable to the similar model of other languages' handwriting recognition systems. © 2022, CC BY.

关键词： Convolutional neural networks

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