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Articles 17821 - 17850 of 63037
Full-Text Articles in Computer Sciences
Case Study Of Scrum Methodology As Used By A Capstone Team, Lilly I. Yeaton
Case Study Of Scrum Methodology As Used By A Capstone Team, Lilly I. Yeaton
University Honors Theses
Scrum is widely used in the software industry to manage all kinds of projects. This case study examines the way in which a capstone team used the methodology and models the specific project management processes they used over the course of their project. These models and the process modifications therein are then compared to the team’s velocity at different points in the project. The results of this analysis suggest a correlation between asynchronous daily meetings and sprint reviews and improved velocity.
Covid-19 Multi-Targeted Drug Repurposing Using Few-Shot Learning, Yang Liu, You Wu, Xiaoke Shen, Lei Xie
Covid-19 Multi-Targeted Drug Repurposing Using Few-Shot Learning, Yang Liu, You Wu, Xiaoke Shen, Lei Xie
Publications and Research
The life-threatening disease COVID-19 has inspired significant efforts to discover novel therapeutic agents through repurposing of existing drugs. Although multi-targeted (polypharmacological) therapies are recognized as the most efficient approach to system diseases such as COVID-19, computational multi-targeted compound screening has been limited by the scarcity of high-quality experimental data and difficulties in extracting information from molecules. This study introduces MolGNN , a new deep learning model for molecular property prediction. MolGNN applies a graph neural network to computational learning of chemical molecule embedding. Comparing to state-of-the-art approaches heavily relying on labeled experimental data, our method achieves equivalent or superior prediction …
Characterizing The Vibrational Spectra Of Hydrogen Bonded Systems With Molecular Dynamics Simulations And Quantum Chemical Methods, Dalton Boutwell
Characterizing The Vibrational Spectra Of Hydrogen Bonded Systems With Molecular Dynamics Simulations And Quantum Chemical Methods, Dalton Boutwell
Master of Science in Chemical Sciences Theses
We apply and assess the utility of DMD for the purpose of investigating complex spectral features in N2H+···OC, N2D+···OC, C2O4H-, C2O4D- and (HCOOH)2. The proton transfer as a vibrational motion consists of diffuse qualities that can be accounted for with classical and quantum chemical analyses. Classical approaches yield a wealth of information about vibrational spectra at a reduced cost, as in the case of previously investigated N4H+. The isoelectronic N2H+···OC has …
Data-Driven Artificial Intelligence For Calibration Of Hyperspectral Big Data, Vasit Sagan, Maitiniyazi Maimaitijiang, Sidike Paheding, Sourav Bhadra, Nichole Gosselin, Max Burnette, Jeffrey Demieville, Sean Hartling, David Lebauer, Maria Newcomb, Duke Pauli, Kyle T. Peterson, Nadia Shakoor, Abby Stylianou, Charles S. Zender, Todd C. Mockler
Data-Driven Artificial Intelligence For Calibration Of Hyperspectral Big Data, Vasit Sagan, Maitiniyazi Maimaitijiang, Sidike Paheding, Sourav Bhadra, Nichole Gosselin, Max Burnette, Jeffrey Demieville, Sean Hartling, David Lebauer, Maria Newcomb, Duke Pauli, Kyle T. Peterson, Nadia Shakoor, Abby Stylianou, Charles S. Zender, Todd C. Mockler
Michigan Tech Publications, Part 1
Near-earth hyperspectral big data present both huge opportunities and challenges for spurring developments in agriculture and high-throughput plant phenotyping and breeding. In this article, we present data-driven approaches to address the calibration challenges for utilizing near-earth hyperspectral data for agriculture. A data-driven, fully automated calibration workflow that includes a suite of robust algorithms for radiometric calibration, bidirectional reflectance distribution function (BRDF) correction and reflectance normalization, soil and shadow masking, and image quality assessments was developed. An empirical method that utilizes predetermined models between camera photon counts (digital numbers) and downwelling irradiance measurements for each spectral band was established to perform …
Clustering And Neighbouring Technique Based Energy-Efficient Routing For Wsns, Ahmed Adil Alkadhmawee, Mohanad Abdulkareem Hasan Hasab, Enas Wahab Abood
Clustering And Neighbouring Technique Based Energy-Efficient Routing For Wsns, Ahmed Adil Alkadhmawee, Mohanad Abdulkareem Hasan Hasab, Enas Wahab Abood
Karbala International Journal of Modern Science
Energy efficiency is the main prerequisite for the permanent and reliable operation of wireless sensor networks (WSNs). Clustering techniques are designed to build energy-efficient networks that enhance network lifetime. Clustering poses certain challenges that directly affect the network performance such as the cluster head selection and routing process. This paper proposes an approach called Clustering and Neighbouring technique Based Energy-Efficient Routing (CNBEER). The CNBEER approach utilises the clustering algorithm and neighbouring technique to prolong the network lifetime by reducing its total energy consumption. The clustering method divides a network into equal-sized clusters to mitigate inessential energy consumption. Moreover, the clustering …
Influence Maximization Based On A Non-Dominated Sorting Genetic Algorithm, Elaf Adel Abbas, Huda Naji Nawaf
Influence Maximization Based On A Non-Dominated Sorting Genetic Algorithm, Elaf Adel Abbas, Huda Naji Nawaf
Karbala International Journal of Modern Science
Influence Maximization (IM) is a problem represented by a set of users who are specified in advance and are usually called the seed. The latter can influence their friends, who can in turn influence others and so on until it reaches the largest number of users within the network. This issue is of ultimate importance in a variety of fields. In the current study, a Non-dominated Sorting Genetic Algorithm II (NSGA-II) has been adopted in influence maximization to produce the so-called NSGAII based IM algorithm (NSGAII-IM). Principally, the population should be represented with individuals of variable lengths as the seed …
Automatic Detection Of Citrus Fruit And Leaves Diseases Using Deep Neural Network Model, Asad Khattak, Muhammad Usama Asghar, Ulfat Batool, Muhammad Zubair Asghar, Hayat Ullah, Mabrook Al-Rakhami, Abdu Gumaei
Automatic Detection Of Citrus Fruit And Leaves Diseases Using Deep Neural Network Model, Asad Khattak, Muhammad Usama Asghar, Ulfat Batool, Muhammad Zubair Asghar, Hayat Ullah, Mabrook Al-Rakhami, Abdu Gumaei
All Works
Citrus fruit diseases are the major cause of extreme citrus fruit yield declines. As a result, designing an automated detection system for citrus plant diseases is important. Deep learning methods have recently obtained promising results in a number of artificial intelligence issues, leading us to apply them to the challenge of recognizing citrus fruit and leaf diseases. In this paper, an integrated approach is used to suggest a convolutional neural networks (CNNs) model. The proposed CNN model is intended to differentiate healthy fruits and leaves from fruits/leaves with common citrus diseases such as Black spot, canker, scab, greening, and Melanose. …
Understanding Ransomware Trajectory To Create An Informed Prediction, J. D. Klusnick
Understanding Ransomware Trajectory To Create An Informed Prediction, J. D. Klusnick
University Honors Theses
Ransomware is a form of extortion in which digital files are rendered inaccessible until a ransom payment is made. Modern ransomware emerged in 2006 and its destructive influence has been expanding ever since. In recent years cybercriminals have evolved who they target, what computer systems they target, and how they infect those systems. Meanwhile, cybersecurity experts have modelled ransomware methods allowing them to innovate their defense techniques across three paradigms: recovery, detection, and prevention. Ultimately either ransomware attackers or ransomware defenders will dominate this ongoing conflict. A review of the literature indicates that the ransomware crime wave will likely be …
Characterizing Soil Stiffness Using Thermal Remote Sensing And Machine Learning, Jordan Ewing, T. Oommen, Paramsothy Jayakumar, Russell Alger
Characterizing Soil Stiffness Using Thermal Remote Sensing And Machine Learning, Jordan Ewing, T. Oommen, Paramsothy Jayakumar, Russell Alger
Michigan Tech Publications, Part 1
Soil strength characterization is essential for any problem that deals with geomechanics, including terramechanics/terrain mobility. Presently, the primary method of collecting soil strength parameters through in situ measurements but sending a team of people out to a site to collect data this has significant cost implications and accessing the location with the necessary equipment can be difficult. Remote sensing provides an alternate approach to in situ measurements. In this lab study, we compare the use of Apparent Thermal Inertia (ATI) against a GeoGauge for the direct testing of soil stiffness. ATI correlates with stiffness, so it allows one to predict …
Rebalancing Shared Mobility Systems By User Incentive Scheme Via Reinforcement Learning, Matthew Brian Schofield
Rebalancing Shared Mobility Systems By User Incentive Scheme Via Reinforcement Learning, Matthew Brian Schofield
Theses and Dissertations
Shared mobility systems regularly suffer from an imbalance of vehicle supply within the system, leading to users being unable to receive service. If such imbalance problems are not mitigated some users will not be serviced. There is an increasing interest in the use of reinforcement learning (RL) techniques for improving the resource supply balance and service level of systems. The goal of these techniques is to produce an effective user incentivization policy scheme to encourage users of a shared mobility system to slightly alter their travel behavior in exchange for a small monetary incentive. These slight changes in user behavior …
Windows Kernel Hijacking Is Not An Option: Memoryranger Comes To The Rescue Again, Igor Korkin
Windows Kernel Hijacking Is Not An Option: Memoryranger Comes To The Rescue Again, Igor Korkin
Journal of Digital Forensics, Security and Law
The security of a computer system depends on OS kernel protection. It is crucial to reveal and inspect new attacks on kernel data, as these are used by hackers. The purpose of this paper is to continue research into attacks on dynamically allocated data in the Windows OS kernel and demonstrate the capacity of MemoryRanger to prevent these attacks. This paper discusses three new hijacking attacks on kernel data, which are based on bypassing OS security mechanisms. The first two hijacking attacks result in illegal access to files open in exclusive access. The third attack escalates process privileges, without applying …
Adapting An Agent-Based Model Of Infectious Disease Spread In An Irish County To Covid-19, Elizabeth Hunter, John D. Kelleher
Adapting An Agent-Based Model Of Infectious Disease Spread In An Irish County To Covid-19, Elizabeth Hunter, John D. Kelleher
Articles
The dynamics that lead to the spread of an infectious disease through a population can be characterized as a complex system. One way to model such a system, in order to improve preparedness, and learn more about how an infectious disease, such as COVID-19, might spread through a population, is agent-based epidemiological modelling. When a pandemic is caused by an emerging disease, it takes time to develop a completely new model that captures the complexity of the system. In this paper, we discuss adapting an existing agent-based model for the spread of measles in Ireland to simulate the spread of …
Algebraic Graph-Assisted Bidirectional Transformers For Molecular Property Prediction, Dong Chen, Kaifu Gao, Duc Duy Nguyen, Xin Chen, Yi Jiang, Guo-Wei Wei, Feng Pan
Algebraic Graph-Assisted Bidirectional Transformers For Molecular Property Prediction, Dong Chen, Kaifu Gao, Duc Duy Nguyen, Xin Chen, Yi Jiang, Guo-Wei Wei, Feng Pan
Mathematics Faculty Publications
The ability of molecular property prediction is of great significance to drug discovery, human health, and environmental protection. Despite considerable efforts, quantitative prediction of various molecular properties remains a challenge. Although some machine learning models, such as bidirectional encoder from transformer, can incorporate massive unlabeled molecular data into molecular representations via a self-supervised learning strategy, it neglects three-dimensional (3D) stereochemical information. Algebraic graph, specifically, element-specific multiscale weighted colored algebraic graph, embeds complementary 3D molecular information into graph invariants. We propose an algebraic graph-assisted bidirectional transformer (AGBT) framework by fusing representations generated by algebraic graph and bidirectional transformer, as well as …
Turkic Interlingua: A Case Study Of Machine Translation In Low-Resource Languages, Jamshidbek Mirzakhalov
Turkic Interlingua: A Case Study Of Machine Translation In Low-Resource Languages, Jamshidbek Mirzakhalov
USF Tampa Graduate Theses and Dissertations
Machine Translation (MT) has the potential to bridge the gap between the developed world and the marginalized communities by making information more accessible in real-time. While there are over 7000 spoken languages in the world, only about a hundred have access to high-quality MT systems and even fewer enjoy the benefits of more advanced language technologies. Unfortunately, resource scarcity and the lack of digital infrastructure are only some of the many challenges associated with globalizing NLP. Many large-scale multilingual studies and datasets often get little to no feedback from native speakers or linguistic experts of the languages involved, leading to …
Development And Prospect Of Simulation Research In China, Xiaogang Qiu, Duan Hong, Xie Xu, Mengna Zhu
Development And Prospect Of Simulation Research In China, Xiaogang Qiu, Duan Hong, Xie Xu, Mengna Zhu
Journal of System Simulation
Abstract: It is important to treat modeling and simulation (M&S) as a discipline to advance its development. In China, M&S has been researched and applied over 40 years, and is gradually becoming an independent discipline. Modeling and simulating the complex systems are the challenge, however, this also brings grand opportunity for M&S to widen and improve itself. Since 1980s, the M&S community in China has realized the broad applications of M&S, comprehended M&S from multiple perspectives, conducted extensive research on basic questions, and discussed the composition of the basic simulation theory. By summarizing these achievements, the future works that have …
Multiple Object Tracking And Kinematic Simulation For Short Track Speed Skating, Li Qi, Hanlin Mo, Xiangdong Wang, Li Hua
Multiple Object Tracking And Kinematic Simulation For Short Track Speed Skating, Li Qi, Hanlin Mo, Xiangdong Wang, Li Hua
Journal of System Simulation
Abstract: Aiming at the problem that it is difficult to obtain the motion data of each athlete in the short track speed skating competition, an algorithm flow of multiple object tracking and kinematic simulation is proposed. A local matching metric is proposed to deal with the partial occlusion in monocular video and improve the tracking stability and robustness. The motion simulation method based on homography mapping and derivative of fitted curve is realized to estimate kinematic parameters such as velocity and acceleration. The experiments on Skating Track Multiple ObjectTracking (STMOT) verified the effectiveness and superiority of the proposed methods.
Decomposition Furnace Outlet Temperature Prediction Based On Elasticnet And Lstm, Guangyu Yu, Xueping Dong, Xiangmin Wang, Gan Min
Decomposition Furnace Outlet Temperature Prediction Based On Elasticnet And Lstm, Guangyu Yu, Xueping Dong, Xiangmin Wang, Gan Min
Journal of System Simulation
Abstract: The outlet temperature of the decomposition furnace is a key indicator in the cement production process. Aiming at the problem that traditional prediction methods only consider the influence of wind, coal, and materials, a temperature prediction model of ElasticNet combined with Long Short-Term Memory (LSTM) neural network is proposed. The ElasticNet-LSTM export temperature prediction model is constructed by using the ElasticNet method to estimate the parameters of different variables, fully considering the influencing factors and realizing the variable screening, and analyzing the influence of the number of hidden layers and nodes on the accuracy of the neural network. Simulation …
A Shared Memory Based Parallel Hierarchical Interest Matching Algorithm, Wenjie Tang, Junwei Cheng, Yiping Yao, Zhu Feng
A Shared Memory Based Parallel Hierarchical Interest Matching Algorithm, Wenjie Tang, Junwei Cheng, Yiping Yao, Zhu Feng
Journal of System Simulation
Abstract: Interest matching plays an important role in distributed simulation. However, because of huge number of simulation entities and frequent change of regions, interest matching consumes tremendous computation in large scale simulations. The ubiquity of multicore urges us to improve the performance of interest matching by parallelization. A shared memory based parallel hierarchical interest matching algorithm is propose to solve the problem. It maps subscribe regions into a full binary tree, and compares update regions with the tree in parallel. Due to the associative relationship between adjacent nodes, unnecessary comparisons can be eliminated. The experimental results demonstrate that the …
Time-Delay Estimation For Mimo Delay Systems With Unknown Structures, Xuguang Wang, Mengjie Feng, Su Jie, Jiale Quan
Time-Delay Estimation For Mimo Delay Systems With Unknown Structures, Xuguang Wang, Mengjie Feng, Su Jie, Jiale Quan
Journal of System Simulation
Abstract: In the unknown-structure delay system modeling, time-delays lead to a mismatch between the system inputs and outputs on the timeline and cause low system modeling accuracy. A time-delay estimation method is proposed for the unknown-structure MIMO (Multiple-Input Multiple-Output) delay systems. A general mathematical description is given from the algebraic point of view, the delay correlation function is defined, and a quantitative constraint between system input-output and time-delay is constructed. A greedy time-delay search algorithm is introduced based on the delay correlation function. Simulation experiments and real data experiments show the availability of the proposed method.
Fault Detection Of Wind Turbine Bearing Based On Bo-Sdae Multi-Source Signal, Dinghui Wu, Zhichao Zhu, Xinhong Han
Fault Detection Of Wind Turbine Bearing Based On Bo-Sdae Multi-Source Signal, Dinghui Wu, Zhichao Zhu, Xinhong Han
Journal of System Simulation
Abstract: Due to the discrepancy within signals from sensors of wind turbines caused by environmental interference, the fault detection results of wind turbine bearing will be affected and the multi-source signal fault diagnosis method is proposed to improve the reliability of fault detection. The time-domain and frequency-domain features of bearing vibration signals, noise signals and temperature signals are used for feature extraction,and then the features are transmitted to the stacked denoising autoencoders, which are optimized the hidden layer node structure by the Bayesian optimization algorithm to achieve multi-source signal feature fusion. Softmax function is used for classification. Experiments show that …
Rapid Development Technology Of Virtual Maintenance Training Simulation Model For Aviation Equipment, Rongqiang Li, Aibing Wen, Bin Hua, Jiajun Li, Jiang Bing
Rapid Development Technology Of Virtual Maintenance Training Simulation Model For Aviation Equipment, Rongqiang Li, Aibing Wen, Bin Hua, Jiajun Li, Jiang Bing
Journal of System Simulation
Abstract: Taking the practical application of the aircraft virtual maintenance training as the demand, the basic development process and technical means of the aircraft virtual maintenance training simulation model are analyzed, which solves the technical bottleneck in the popularization and application of the large-scale engineering. Aiming at the crucial problems such as the low efficiency in the development of maintenance training simulation model and the difficulties in updating the model, a rapid development process to support the large-scale engineering applications is proposed. The principles and implementation ways of three key technologies supporting the rapid development of virtual maintenance training simulation …
Day-Ahead Dispatching Model Of Source-Load Coordination Based On Response Behavior To Real-Time Pricing, Liu Yun, Han Song, Qiuli Huang
Day-Ahead Dispatching Model Of Source-Load Coordination Based On Response Behavior To Real-Time Pricing, Liu Yun, Han Song, Qiuli Huang
Journal of System Simulation
Abstract: In order to give full play to the regulating role of electricity price on the market, considering the difference of consumers' response behavior to real-time pricing, consumers are divided into short-term consumers, mixed consumers and long-term consumers, and the real-time pricing models of three types of consumers are constructed based on the electricity price elasticity matrix. On this basis, a day-ahead dispatching model of source-load coordination based on consumer’s response behavior to real-time pricing is established. With the help of the MOST toolkits in MATPOWER and the Mosek solver, the arithmetic analysis is developed in a modified IEEE 57-buses …
Spatial Knowledge Representation Model Of Simulation Scenario Based On Ontology, Zhu Jie, Hongjun Zhang
Spatial Knowledge Representation Model Of Simulation Scenario Based On Ontology, Zhu Jie, Hongjun Zhang
Journal of System Simulation
Abstract: Military simulation scenario has abundant spatial knowledge and is closely related to various models of combat simulation. In view of the lack of uniform specification for the description of simulation scenario knowledge, it is necessary to construct a spatial knowledge representation model in line with spatial thinking to effectively analyze the spatial entities and their interrelationships in simulation scenario. The ontology method is used to establish spatial knowledge domain ontology and form the formal description specification of spatial knowledge concept; the spatial knowledge structure is described hierarchically by using the method of concept knowledge tree, clarifying the semantic logic …
Performance Analysis Of Cognitive Cooperative Systems Based On Quadrature Spatial Modulation, Guo Hui, Xuejiao Guo, Ting Qiao, Liu Meng
Performance Analysis Of Cognitive Cooperative Systems Based On Quadrature Spatial Modulation, Guo Hui, Xuejiao Guo, Ting Qiao, Liu Meng
Journal of System Simulation
Abstract: The cognitive cooperative communication system based on quadrature spatial modulation (QSM) is proposed to solve the problems of inter-channel interference and inter-antenna synchronization. The mean power allocation algorithm is used to analyze performance of the system. In addition, the relay nodes and the destination node decode their received signals with the maximum likelihood detection algorithm. The exact closed expressions of outage probability under random relay selection (RRS), suboptimal relay selection (SRS) and optimal relay selection (ORS) are derived respectively. The approximate outage probabilities in the high signal-to-noise ratio region are derived, and the diversity gains of three relay …
An Improved Differential Evolution Algorithm For Fractional Order System Identification, Yu Wei, Henghui Liang, Luo Ying
An Improved Differential Evolution Algorithm For Fractional Order System Identification, Yu Wei, Henghui Liang, Luo Ying
Journal of System Simulation
Abstract: In order to build a high-precision fractional-order model, which needs to identify more parameters, an improved differential evolution algorithm is proposed for the identification of fractional-order systems. In the mutation strategy, the basis vector is randomly selected from the optimal individual population, and the scaling factor and cross-probability factor are adaptively adjusted according to the information of the successfully mutated individual during the search process to improve the exploration and mining capabilities of the algorithm. By solving the five test functions, the improved algorithm is proved to have strong solving ability. Taking the fractional-order model of permanent magnet synchronous …
Auxiliary Application Of Wearable Computing Equipment In Industrial Intelligent Operation And Maintenance, Shen Yi, Wu Gang, Zhou Rui
Auxiliary Application Of Wearable Computing Equipment In Industrial Intelligent Operation And Maintenance, Shen Yi, Wu Gang, Zhou Rui
Journal of System Simulation
Abstract: Virtual operation and intelligent operation and maintenance based on wearable integrated computing devices have become a trend of lean industrial production. The operation and maintenance efficiency of tobacco production equipment can be directly improved by using mixed reality, virtual simulation and other technologies, combined with virtual interactive assistance applications . Combining 5G, IoT and big data technologies, a system design of wearable smart devices is proposed based on smart glasses with multi-dimensional spatial information synchronization, computer vision and natural perceptual interaction, and a set of design scheme for auxiliary operation and maintenance of cigarette equipment is innovated. The intelligent …
Landscape-Based Mutational Sensitivity Cartography And Network Community Analysis Of The Sars-Cov-2 Spike Protein Structures: Quantifying Functional Effects Of The Circulating D614g Variant, Gennady M. Verkhivker, Steve Agajanian, Deniz Yasar Oztas, Grace Gupta
Landscape-Based Mutational Sensitivity Cartography And Network Community Analysis Of The Sars-Cov-2 Spike Protein Structures: Quantifying Functional Effects Of The Circulating D614g Variant, Gennady M. Verkhivker, Steve Agajanian, Deniz Yasar Oztas, Grace Gupta
Mathematics, Physics, and Computer Science Faculty Articles and Research
We developed and applied a computational approach to simulate functional effects of the global circulating mutation D614G of the SARS-CoV-2 spike protein. All-atom molecular dynamics simulations are combined with deep mutational scanning and analysis of the residue interaction networks to investigate conformational landscapes and energetics of the SARS-CoV-2 spike proteins in different functional states of the D614G mutant. The results of conformational dynamics and analysis of collective motions demonstrated that the D614 site plays a key regulatory role in governing functional transitions between open and closed states. Using mutational scanning and sensitivity analysis of protein residues, we identified the stability …
Discussing Digital Twin From Of Modeling And Simulation, Zhang Lin, Lu Han
Discussing Digital Twin From Of Modeling And Simulation, Zhang Lin, Lu Han
Journal of System Simulation
Abstract: The development and evolution of modeling and simulation technology, and its importance in scientific and technological progress are briefly reviewed. The intrinsic relation between digital twin and modeling and simulation is revealed by analyzing the background and concept of digital twin. The way to build and evaluate a digital twin based on modeling and simulation theoretical methods is discussed. to ensure the credibility.
Multi-View Human Action Recognition Based On Deep Neural Network, Zhao Ying, Lu Yao, Zhang Jian, Qidi Liang, Long Wei
Multi-View Human Action Recognition Based On Deep Neural Network, Zhao Ying, Lu Yao, Zhang Jian, Qidi Liang, Long Wei
Journal of System Simulation
Abstract: A novel deep neural network named CNN+CA(Convolutional Neural Network plus Context Attention) model is constructed and a new recognition algorithm based on sequence matching is presented to improve the recognition accuracy of MVHAR (Multi-view Human Action Recognition). A CNN(Convolutional Neural Network) is designed to automatically learn multi-view fusion features; the CA (Context Attention) module is introduced to selectively focus on the parts of the features that are relevant for the recognition task; the proposed recognition algorithm based on sequence matching is used to realize MVHAR. The experimental results on the IXMAS dataset and the i3DPost dataset …