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Flexibility and rigidity index for chromosome packing,flexibility and dynamics analysis

Frontiers of Computer science 2022年第4期16卷 203-213页

作者： Jiajie Peng Jinjin Yang D Vijay Anand Xuequn Shang Kelin Xia School of Computer Science Northwestern Polytechnical UniversityXi’an710129China Key Laboratory of Big Data Storage and Management Northwestern Polytechnical UniversityMinistry of Industry and Information TechnologyXi’an710129China Division of Mathematical Sciences School of Physical and Mathematical SciencesNanyang Technological UniversitySingapore637371Singapore School of Biological Sciences Nanyang Technological UniversitySingapore637371Singapore

The packing of genomic DNA from double helix into highly-order hierarchical assemblies has a great impact on chromosome flexibility,dynamics and *** open and accessible regions of chromosomes are primary binding positions for regulatory elements and are crucial to nuclear processes and biological *** by the success of flexibility-rigidity index(FRI)in biomolecular flexibility analysis and drug design,we propose an FRI-based model for quantitatively characterizing chromosome *** on Hi-C data,a flexibility index for each locus can be ***,flexibility is tightly related to packing *** compacted regions are usually more rigid,while loosely packed regions are more ***,a strong correlation is found between our flexibility index and DNase and ATAC values,which are measurements for chromosome *** addition,the genome regions with higher chromosome flexibility have a higher chance to be bound by transcription ***,the Gaussian network model(GNM)is applied to analyze the chromosome accessibility and a mobility profile has been proposed to characterize chromosome *** with GNM,our FRI is slightly more accurate(1%to 2%increase)and significantly more efficient in both computational time and *** a 5Kb resolution Hi-C data,the flexibility evaluation process only takes FRI a few minutes on a single-core *** contrast,GNM requires 1.5 hours on 10 ***,interchromosome interactions can be easily combined into the flexibility evaluation,thus further enhancing the accuracy of our *** contrast,the consideration of interchromosome information into GNM will significantly increase the size of its Laplacian(or Kirchhoff)matrix,thus becoming computationally extremely challenging for the current *** software and supplementary document are available at https://***/jiajiepeng/FRI_chrFle.

关键词： flexibility-rigidity index 3D genome chromosome flexibility

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Network Situation Awareness Model Based on Incomplete Information Game 5th

Network Situation Awareness Model Based on Incomplete Inform...

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5th International Conference on Security and Privacy in New Computing Environments, SPNCE 2022

作者： Zhang, Hongbin Yin, Yan Zhao, Dongmei Liu, Bin Wang, Yanxia Liu, Zhen School of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang050000 China Hebei Key Laboratory of Network and Information Security Hebei Normal University Hebei Shijiazhuang050024 China School of Economics and Management Hebei University of Science and Technology Shijiazhuang050000 China Research Center of Big Data and Social Computing Hebei University of Science and Technology Shijiazhuang050000 China Hebei Geological Workers’ University Shijiazhuang050081 China

ISBN: (纸本)9783031306228

Game theory has been widely used in network security situational awareness. However, most of the currently proposed game-based offensive and defensive situational awareness methods are for traffic data, and there are fewer models or methods for analysis using vulnerability data. To overcome these issues, this paper proposes collecting periodic security vulnerability information in the network and utilizing the change in vulnerability status to achieve network security situational awareness. At this time, a network attack and defense game model based on incomplete information is proposed, which uses the state changes of the vulnerability life cycle to model the attack and defense behavior, calculates the benefits of both attack and defense through the evaluation of the exploitability of the vulnerability, and then quantifies the security situation value. We carried out the experiments using the vulnerability dataset, which was obtained by scanning the IP addresses of several enterprises in Hebei Province, China. The experimental results show that the approach of using network security vulnerabilities to assess network security status is feasible. © 2023, ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Network security

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Fall Detection in Elderly People Using Compact Wearable Sensors with AI 13

Fall Detection in Elderly People Using Compact Wearable Sens...

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13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Venkateswaran, R. Babu, B.N.V. Madhu Parameswari, M. Vamsilala, Purushothapatnapu Naga Venkata Ambigaipriya, S. Keerthana, N.V. College of Computing and Information Sciences University of Technology and Applied Sciences Cyber and Information Security Salalah Oman Teegala Krishna Reddy Engineering College Department of Cse Telangana Hyderabad India Sona College of Technology Department of Information Technology Tamilnadu India Vignan's Lara Institute of Technology and Science Andra Pradesh Vadlamudi Guntur India Kongunadu College of Engineering and Technology Department of Artificial Intelligence and Data Science Tamil Nadu Trichy India Velalar College of Engineering and Technology Department of Information Technology Tamil Nadu Erode India

ISBN: (纸本)9798350380583

The most crucial public concern regarding health is elderly people's falls which tend to undesirable injuries leading to an increase in healthcare expenses for them. This research paper describes a method that uses AI technology with a barometer sensor to detect falls in elderly people. In addition to this, the AI model is trained on a diverse dataset comprising various activities of daily living, enabling it to distinguish between normal movements and those that may lead to falls. This method probably gives some notifications or missed calls to the caretakers as well as to the hospitals. When compared to many more sensors such as gyroscopes and some fitness trackers, the Apple Watch 4, and Samsung Gear S3 barometer sensor perform in a better better way. Key performance metrics such as absolute and altitude are calculated and compared based on a natural language processing (NLP) algorithm. © 2024 IEEE.

关键词： Barometers

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Automated Detection of Poultry Farms from Aerial Images for Actionable AI System toward Biosecurity Applications

Automated Detection of Poultry Farms from Aerial Images for ...

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2023 IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2023

作者： Li, Yu Das, Rana Duong, Cuong Lim, Teng-Teeh Haithcoat, Timothy Ersoy, Ilker Shyu, Chi-Ren University of Missouri Department of Electrical Engineering and Computer Science ColumbiaMO United States University of Missouri Biological Engineering ColumbiaMO United States University of Missouri Plant Science and Technology Extension ColumbiaMO United States University of Missouri Institute for Data Science and Informatics Columbia United States University of Missouri Institute for Data Science and Informatics Electrical Eng. and Computer Science Dept. ColumbiaMO United States

ISBN: (纸本)9798350359527

Poultry productions have shifted towards larger farms and often cluster in certain regions. However, many of the smaller farms with a considerable amount of production are not considered concentrated animal feeding operation under regulation in many regions, and therefore are not required to obtain a permit. As a result, information of these operations is not being recorded and monitored, contributing to an increased risk for biosecurity issues. The development in computer vision and availability of high-resolution remote sensing imagery allows the possibility for automatic localization and operation estimation of the unregistered farms, filling in the missing information needed for biosecurity risk management. The farm information extracted from images can be incorporated with other available geospatial data, such as road networks, watersheds, etc., to build a comprehensive knowledge base for actionable plans. In the current study, we present a framework for automated localization, analysis, and visualization of poultry farms from remote sensing images. A robust machine learning method was developed to computationally link the visual features from remote sensing imagery to Missouri department of Natural Resources Concentrated Animal Feeding Operation data, to identify the animal farm type, location, and potential capacity in the region. The developed process can be performed automatically on images of different timestamps to analyze temporal and spatial development of poultry production facilities of a region. This research contributes to the effective management and regulation of poultry and potentially other animal farms, providing insights into farm characteristics and capacities while facilitating informed decision-making. © 2023 IEEE.

关键词： Object detection

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Critical (P5, dart) -Free Graphs 16th

Critical (P5, dart) -Free Graphs

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16th Annual International Conference on Combinatorial Optimization and Applications, COCOA 2023

作者： Xia, Wen Jooken, Jorik Goedgebeur, Jan Huang, Shenwei College of Computer Science Nankai University Tianjin300071 China Tianjin Key Laboratory of Network and Data Security Technology Nankai University Tianjin300071 China Department of Computer Science KU Leuven Campus Kulak-Kortrijk Kortrijk8500 Belgium Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent9000 Belgium

ISBN: (纸本)9783031496134

Given two graphs H1 and H2, a graph is (H1, H2) -free if it contains no induced subgraph isomorphic to H1 nor H2. A dart is the graph obtained from a diamond by adding a new vertex and making it adjacent to exactly one vertex with degree 3 in the diamond. In this paper, we show that there are finitely many k-vertex-critical (P5, dart) -free graphs for k≥ 1. To prove these results, we use induction on k and perform a careful structural analysis via Strong Perfect Graph Theorem combined with the pigeonhole principle based on the properties of vertex-critical graphs. Moreover, for k∈ { 5, 6, 7 } we characterize all k-vertex-critical (P5, dart) -free graphs using a computer generation algorithm. Our results imply the existence of a polynomial-time certifying algorithm to decide the k-colorability of (P5, dart) -free graphs for k≥ 1 where the certificate is either a k-coloring or a (k+ 1 ) -vertex-critical induced subgraph. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Polynomial approximation

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Importance of alternative pathways to STEM through analysis of longitudinal student dataset

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Discover Education 2024年第1期3卷 1-18页

作者： AlHmoud, Issa W. Poudel, Samin Deshmukh, Sulochana Booth, Caroline S. Monty, Greg Bikdash, Marwan Department of Computational Data Science and Engineering North Carolina A & T State University Greensboro NC United States Department of Counseling North Carolina A & T State University Greensboro NC United States Center for Energy Research and Technology North Carolina A & T State University Greensboro NC United States

Using a longitudinal national educational dataset, data science methods were applied to explain students’ educational trajectories and determine the most predictive variables in STEM degree attainment. Challenging the notion of the STEM pipeline, an Alternative Pathways to STEM (APS) model was proposed. Using a foundation of Social Cognitive Career Theory, academic, demographic, and economic variables were selected for analysis and Imbalanced dataset and random forest data analytic techniques were implemented. Results supported the consideration of the Alternate Pathways to STEM model given the varied educational pathways identified throughout STEM matriculation. Additionally, multiple academic variables, except for career interest, were found to be most predictive of STEM graduation, while demographic and economic variables were not significant. These findings have important implications for recruiting STEM students and increasing STEM graduation rates. Additional research is recommended using these data extraction and analysis methodologies on other datasets to further test the efficacy of the Alternative Pathways to STEM model. © The Author(s) 2024.

关键词： Education Imbalanced dataset Pathways Random forest STEM STEM pipeline

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Path Planning of Mission-driven USVs Based on Stochastic Schemata Exploiter 4

Path Planning of Mission-driven USVs Based on Stochastic Sch...

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4th International Conference on data-Driven Optimization of Complex Systems, DOCS 2022

作者： Zhi, Liwei Zuo, Yi Li, Tieshan Dalian Maritime University Collaborative Innovation Center of Maritime Big Data & Artificial General Intelligence Navigation College Dalian China University of Electronic Science and Technology of China School of Automatic Engineering Chengdu China

ISBN: (数字)9781665459822

ISBN: (纸本)9781665459822

In recent years, the research on unmanned surface vessels (USVs) has become a new hot spot in the development of intelligent applications in the shipping industry of various countries. With the gradual maturity and popularization of modern information technologies such as computer technology and artificial intelligence, the research on USVs has scientific and technological support, and is developing from theoretical research to application practice. Among them, the autonomous assignment and decision-making coordination of multi-target tasks is one of the important research topics for autonomous navigation of USVs. At the same time, the path planning of UAVs also plays a crucial role in autonomous assignment and navigation. In view of the above points, this paper proposes a heuristic optimization algorithm based on stochastic schemata exploiter (SSE). It is used to solve the path optimization problem of USVs for mission requirements. In the experiment, two combinations of two USVs are considered, and the paths are optimized in the two cases of jointly completing the task and cooperatively completing the task. The algorithm combines the heuristic information of the distribution structure of static obstacles in the environment, expands the obstacles and optimizes the node selection of the USV, so that the USV can avoid the obstacles with a higher probability and find an excellent feasible path, and improve the global search ability. Compared with the genetic algorithm (GA) through simulation experiments, the proposed algorithm presents a significant improvement in timeliness and path optimization. © 2022 IEEE.

关键词： Motion planning

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Structured variational approximations with skew normal decomposable graphical models

arXiv

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arXiv 2023年

作者： Salomone, Robert Yu, Xuejun Nott, David J. Kohn, Robert Centre for Data Science Queensland University of Technology Australia Department of Statistics and Data Science National University of Singapore Singapore Department of Statistics and Data Science National University of Singapore Institute of Operations Research and Analytics National University of Singapore Singapore Australia

Although there is much recent work developing flexible variational methods for Bayesian computation, Gaussian approximations with structured covariance matrices are often preferred computationally in high-dimensional settings. This paper considers approximate inference methods for complex latent variable models where the posterior is close to Gaussian, but with some skewness in the posterior marginals. We consider skew decomposable graphical models (SDGMs), which are based on the closed skew normal family of distributions, as variational approximations. These approximations can reflect the true posterior conditional independence structure and capture posterior skewness. Different parametrizations are explored for this variational family, and the speed of convergence and quality of the approximation can depend on the parametrization used. To increase flexibility, implicit copula SDGM approximations are also developed, where elementwise transformations of an approximately standardized SDGM random vector are considered. Our parametrization of the implicit copula approximation is novel, even in the special case of a Gaussian approximation. Performance of the methods is examined in a number of real examples involving generalized linear mixed models and state space models, and we conclude that our copula approaches are most accurate, but that the SDGM methods are often nearly as good and have lower computational demands. © 2023, CC BY.

关键词： Importance sampling

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Co-training partial domain adaptation networks for industrial Fault Diagnosis

arXiv

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arXiv 2024年

作者： Chen, Gecheng Luo, Chengwen Li, Jianqiang Chen, Xinkai The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China The Department of Electronic Information Systems Shibaura Institute of Technology Saitama337-8570 Japan

The partial domain adaptation (PDA) challenge is a prevalent issue in industrial fault diagnosis. Current PDA approaches primarily rely on adversarial learning for domain adaptation and use reweighting strategies to exclude source samples deemed outliers. However, the transferability of features diminishes from general feature extraction layers to higher task-specific layers in adversarial learning-based adaptation modules, leading to significant negative transfer in PDA settings. We term this issue the adaptation-discrimination paradox (ADP). Furthermore, reweighting strategies often suffer from unreliable pseudo-labels, compromising their effectiveness. Drawing inspiration from traditional classification settings where such partial challenge is not a concern, we propose a novel PDA framework called Interactive Residual Domain Adaptation Networks (IRDAN), which introduces domain-wise models for each domain to provide a new perspective for the PDA challenge. Each domain-wise model is equipped with a residual domain adaptation (RDA) block to mitigate the ADP problem. Additionally, we introduce a confident information flow via an interactive learning strategy, training the modules of IRDAN sequentially to avoid cross-interference. We also establish a reliable stopping criterion for selecting the best-performing model, ensuring practical usability in real-world applications. Experiments have demonstrated the superior performance of the proposed IRDAN. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Hybrid Machine Learning Approach of "KNN – LSTM" for detection of Brain Tumour in MR images 15

Hybrid Machine Learning Approach of "KNN – LSTM" for detect...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Uganya, G. Gunale, Anuja Govindrao Kulkarni, Shruthi V. Rajalakshmi, D. Valsaraj, Vani Srividhya, B. Department of ECE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Engineering UPL University of Sustainable Technology Block no 402 Ankleshwar-Valia Road Gujarat Vataria India Department of AIML HKBK College of Engineering opposite manyata tech park Nagawara Karnataka Bengaluru India Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computer Application Faculty of Humanities and Sciences SRM Institute Of Science and Technology Tamil Nadu Chengalpattu India

ISBN: (纸本)9798350370249

Now a days, more existing techniques are available to found tumours on brain from MR images. But still the researchers are concentrated on to detect the brain tumour accurately with less time. So, this work proposed the hybrid machine learning algorithm of "K-Nearest Neighbour – Long Short-Term Memory" ("KNN-LSTM") with increased ‘accuracy’, ‘sensitivity’, ‘specificity’, with less ‘time consumption’. In this work, the brain tumour is identified by predicting the MR images from huge dataset with usage of machine learning approach of "KNN-LSTM". Brain tumour detection is difficult when to compare the abnormal and typical tissues of large capacity. In order to enable a speedy recovery following therapy, brain tumours should be identified as soon as feasible. If the brain tumour is discovered early on, patients can benefit from improved care. Due to the irregular spreading of the abrasions, brain tumours are still difficult to identify even with intensive study. Sometimes the tiny tumours look like normal, but it is challenging to find it. So, the techniques are need to increase the classification accuracy, and selecting the useful features. This paper proposes a hybrid machine learning model "KNN-LSTM" for categorizing the tumours with predicting through MR images. MRI brain imaging collection is used for our experimentation. The "KNN" algorithm usage is to extract the important characteristics from pictures once the data has been pre-processed effectively. The proposed work predicts the brain tumour with 99.15% of ‘accuracy’, 98% of ‘sensitivity’, 97.12% of ‘specificity’ and testing time consumption of 0.5 minutes. © 2024 IEEE.

关键词： Image segmentation

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