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19th International Conference on Web Information Systems and Applications, WISA 2022

作者： Xu, Jie Zhou, Jiantao College of Computer Science Engineering Research Center of Ecological Big Data Ministry of Education National and Local Joint Engineering Research Center of Mongolian Intelligent Information Processing Technology Inner Mongolia Cloud Computing and Service Software Engineering Laboratory Inner Mongolia Social Computing and Data Processing Key Laboratory Inner Mongolia Discipline Inspection and Supervision Big Data Key Laboratory Inner Mongolia Big Data Analysis Technology Engineering Laboratory Inner Mongolia University Hohhot China

ISBN: (纸本)9783031203084

Anomaly detection aims to find outliers data that do not conform to expected behaviors in a specific scenario, which is indispensable and critical in current safety environments related studies. However, when performing outlier detection on a large-scale multidimensional dataset, most of the traditional methods make no distinctions between classes of outliers and lack the reasoning ability and the explainability of the analysis results, which leads to the low accuracy of global outlier detection and the inability to effectively identify local outliers. In this paper, we propose a fine-grained anomaly detection method which well utilizes isolation forest and knowledge graph reasoning tactics. First, a better extracting data feature method and a more reasonable weighted strategy are used to find global outliers based on the traditional isolation forest algorithm, and then we construct a custom rule base according to the in-depth research and analysis of global abnormal data, and at last the ontology knowledge is reasoned based on such rule base to achieve the detection of local outliers. Through analysis of extensive experiment results, our method could effectively discover more abnormal data without big loss of time costs, has strong generalization, and correspondingly improves the performance for abnormal detection. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Anomaly detection

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A Reduced State Space Generation Method for Concurrent Systems Based on CPN-PR Model

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IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2024年第6期44卷 2328-2342页

作者： Zhong, Wenjie Sun, Tao Zhou, Jian-Tao Wang, Zhuowei Song, Xiaoyu Inner Mongolia Engineering Laboratory for Big Data Analysis Technology College of Computer Science Inner Mongolia University Engineering Research Center of Ecological Big Data Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Hohhot010000 China Guangdong University of Technology School of Computer Science and Technology Guangzhou China Portland State University Department of Electrical and Computer Engineering PortlandOR97207 United States

Colored Petri Nets (CPNs) provide descriptions of the concurrent behaviors for software and hardware. Model checking based on CPNs is an effective method to simulate and verify the concurrent behavior in system design. However, the model checking method traverses the full state space, which suffers from the state space explosion problem. A reduced state space generation method related to the property of concurrent systems is proposed. Specifically, we extend CPNs to define a property-related model (CPN-PR) and give a property-related analysis method whose results can be used to generate the CPN-PR model. A reduced state space generation method is developed based on enabled binding element filtering rules. The stutter trace equivalence between the state spaces of CPN and CPN-PR has been proven by showing that the reduced state space may not change the model checking result. A comparison experiment is conducted to demonstrate the effectiveness of our method. © 1982-2012 IEEE.

关键词： Model checking

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Cache Characterization of Workloads in a Microservice Environment

Cache Characterization of Workloads in a Microservice Enviro...

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IEEE International Conference on cloud computing in Emerging Markets (CCEM)

作者： Sanjith Athlur Nishant Sondhi Sachit Batra Subramaniam Kalambur Dinkar Sitaram Department of Computer Science Center for Cloud Computing and Big Data PES University Bengaluru

ISBN: (数字)9781728163345

ISBN: (纸本)9781728163352

The advent of Microservice based architecture has lead to the obsolescence of the tedious way of building and deploying applications in a monolithic way. The number of organizations taking advantage of cloud computation and microservice architecture is steadily increasing. From the cloud providers perspective, the purpose of cloud computation is to leverage economies of scale by co-locating several workloads dynamically on the same powerful many-core servers. In this paper, we attempt to characterize cache usage and its impact on performance when multiple workloads are collocated on multicore servers.

关键词： Servers Interference cloud computing Benchmark testing Hardware Multicore processing

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A Face Recognition Method Based on CNN

A Face Recognition Method Based on CNN

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2019 High Performance computing and Computational Intelligence Conference, HPCCI 2019

作者： Xie, Zhiming Li, Junjie Shi, Hui Department of Information Engineering Shanwei Polytechnic Shanwei Guangdong516600 China Research Institute of Cloud Computing and Data Center Project Design Shanwei Innovation Industrial Design and Research Institute Shanwei Guangdong516600 China Professional Committee Cloud Computing and Big Data in Higher Vocational College Higher Education Institute in Guangdong Province Shanwei Guangdong516600 China

The traditional face recognition technology is more complicated for the extraction of facial image features and the selection of classifiers, and the recognition rate is not high. With the continuous maturity of the convolutional neural network from handwritten digit recognition to face recognition, A face recognition algorithm that tests CNN using the Python+Keras framework. The method mainly involves two aspects. One is to observe the influence on the network by changing the number of neurons in the hidden layer;the other is to observe the influence on the network by changing the number of feature maps of the convolutional layer 1 and the convolutional layer 2. The best CNN model is 36-76-1024 through multiple sets of experimental tests. The model can automatically extract facial image features and classify them. Using adam optimizer and softmax classifier for face recognition can make training faster convergence and more. Effectively improve accuracy and use the Dropout method to avoid overfitting. The experimental results show that the recognition rate of the CNN model on the olivettifaces face database is 97.5%. When the optimal CNN model is used, the average recognition rate is close to 100%, which verifies the validity and accuracy of the algorithm and model. © Published under licence by IOP Publishing Ltd.

关键词： Face recognition

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Small Evolution Antenna Design Method Based on Dynamic Hill Climbing Algorithm and Orthogonal Experiment

Small Evolution Antenna Design Method Based on Dynamic Hill ...

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2019 High Performance computing and Computational Intelligence Conference, HPCCI 2019

作者： Shi, Hui Li, Junjie Xie, Zhiming Department of Information Engineering Shanwei Polytechnic Shanwei Guangdong516600 China Research Institute of Cloud Computing and Data Center Project Design Shanwei Innovation Industrial Design and Research Institute Shanwei Guangdong516600 China Professional Committee Cloud Computing and Big Data in Higher Vocational College Higher Education Institute in Guangdong Province Shanwei Guangdong516600 China

Small-scale evolution antennas have the advantages of high gain, wide bandwidth and small size, and have broad application prospects in the aerospace field. Aiming at the complex high-dimensional and dynamic problems of aerial antenna modeling, a dynamic hill-climbing algorithm based on orthogonal design is designed and implemented to simulate and optimize the single-span antenna in high-frequency operation mode. In the optimization problem, this paper defines a new individual comparison mechanism, which can reflect the number of violations of constraints and the degree of violation of each constraint, making the comparison of individuals more reasonable. The antenna simulation results demonstrate the effectiveness and feasibility of using dynamic evolutionary algorithms for antenna design. © Published under licence by IOP Publishing Ltd.

关键词： Antennas

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Using Rough Sets to Improve the High-dimensional data Anomaly Detection Method Based on Extended Isolation Forest 26

Using Rough Sets to Improve the High-dimensional Data Anomal...

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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Liu, Hanlin Zhou, Jiantao Li, Hua Inner Mongolia University College of Computer Science Hohhot China Natl. Loc. Jt. Eng. Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

ISBN: (纸本)9798350331684

Anomaly detection refers to the identification of data objects that deviate from the general data distribution. One of the important challenges in anomaly detection is handling high-dimensional data, especially when it contains a large number of redundant attributes. Although traditional Isolation Forest (iForest) and Extended Isolation Forest (EIF) have performed well in anomaly detection, the detection accuracy is still extremely influenced by redundant attributes. In this paper, we propose a two-tier architecture named RS-EIF, to alleviate the negative impact of redundant attributes. Specifically, the RS-EIF combines the rough set theory and EIF algorithm and provides an effective basis for the EIF-based anomaly detection method by removing redundant attributes with rough set theory. Experimental results show that the RS-EIF algorithm performs better than the iForest and EIF algorithms in terms of anomaly detection rate, and the results of the combination of rough set theory and EIF are more prominent compared to the other combination methods. © 2023 IEEE.

关键词： Anomaly detection

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Herb Pair Danggui-Baishao: Pharmacological Mechanisms Underlying Primary Dysmenorrhea by Network Pharmacology Approach 15th

Herb Pair Danggui-Baishao: Pharmacological Mechanisms Underl...

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15th International Conference on Intelligent computing, ICIC 2019

作者： Li, Li-Ting Qiu, Hai-Yan Liu, Mi-Mi Cai, Yong-Ming Guangdong Pharmaceutical University GuangzhouGuangdong510000 China Guangdong Provincial TCM Precision Medicine Big Data Engineering Technology Research Center GuangzhouGuangdong510000 China Guangdong University College of Precision Medicine Big Data Engineering Research Center Based on Cloud Computing GuangzhouGuangdong510000 China GuangzhouGuangdong510000 China

ISBN: (纸本)9783030269685

Epidemiological studies have shown that primary dysmenorrhea is the most common disease in gynecology, and its incidence rate is between 20% and 90%, which is a common cause of affecting women’s normal work and quality of life. Its high incidence rate, wide spread and economic losses and social harm have caused widespread concern worldwide. The present work adopted a network pharmacology-based approach to provide new insights into the active compounds and therapeutic targets of Danggui-Baishao herb pair for the treatment of primary dysmenorrhea. Fifteen active compounds of the herb pair possessing favorable pharmacokinetic profiles and biological activities were selected, interacting 17 dysmenorrhea-related targets to provide potential synergistic therapeutic actions. Systematic analysis of the constructed networks revealed that these targets such as ABCB1, ESR1, PGR, AKR1C3, PTGS2, CYP2C8, PTGS1 were mainly involved in Steroid hormone biosynthesis, Arachidonic acid metabolism, serotonergic synapse and Ovarian steroidogenesis through steroid metabolic process, steroid hormone mediated signaling pathway, cyclooxygenase pathway, response to estradiol and response to oxidative stress. © 2019, Springer Nature Switzerland AG.

关键词： Metabolism

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Security analysis oriented physical components modeling in quantum key distribution 1

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1st International Symposium on Mobile Internet Security, MobiSec 2016

作者： Mao, Xilong Li, Yan Peng, Yan Zhao, Baokang College of Computer National University of Defense Technology Changsha410073 China Guangxi Colleges and Universities Key Laboratory of Cloud Computing and Complex Systems Guilin University of Electronic Technology Guilin541004 China Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic Technology Guilin541004 China

ISBN: (数字)9789811078507

ISBN: (纸本)9789811078491

Quantum Key Distribution (QKD), based on fundamental principles of quantum mechanics, plays an irreplaceable role in national defense, financial and government affairs. Security analysis of QKD system is of great importance. However, existing studies on modeling QKD system are theory analysis based. In this paper, we propose a Simulation System of Physical Components (SSPC) in QKD system which modeling the three key modules: single photon source, quantum channel and single photon detector, it could generate the simulated key resemble to real QKD physical system and its parameters of the physical components are configurable. Therefore, solution can be deployed in different QKD physical systems. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Quantum cryptography

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Minimally Supervised Sound Event Detection Using a Neural Network

Minimally Supervised Sound Event Detection Using a Neural Ne...

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International Conference on Advances in computing, Communications and Informatics

作者： Aditya Agarwal Syed Munawwar Quadri Savitha Murthy Dinkar Sitaram Center for Cloud Computing and Big Data Computer Science Department PES University Bangalore - India

ISBN: (纸本)9781509020300

This paper proposes a sound event detection system that is trained using a minimally annotated data set of single sounds to identify and separate components of polyphonic sounds. The system uses a Feed Forward Neural Network with a single hidden layer that is pre-trained using an autoencoder. Single sounds, represented as Mel Frequency Cepstral Coefficient (MFCC) feature vectors, are used to train the neural network using back propagation. Polyphonic sounds are pre-processed using Principal Component Analysis (PCA) and Non-negative Matrix Factorization (NMF) to obtain source separated sounds. These source separated sounds are then tested for sound classification using the feed-forward algorithm. Our system is able to achieve reasonable accuracy of source separation and detection with minimal training set. The ultimate goal of our system is to bootstrap from minimal data and learn new sounds leading to a better sound detection system.

关键词： Context Mel frequency cepstral coefficient Artificial neural networks Event detection Source separation Principal component analysis Neurons

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Blockchain-Based Multi-cloud data Storage System Disaster Recovery

Blockchain-Based Multi-Cloud Data Storage System Disaster Re...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Wang, Feiyu Zhou, Jian-Tao College of Computer Science Inner Mongolia University Inner Mongolia Hohhot China Engineering Research Center of Ecological Big Data Ministry of Education Natl. Loc. Jt. Eng. Research Center of Intelligent Information Processing Technology for Mongolian Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology China

ISBN: (纸本)9798350337020

cloud storage services have been used by most businesses and individual users. However, data loss, service interruptions and cyber attacks often lead to cloud storage services not being provided properly, and these incidents have caused financial losses to users. Second, traditional and single-cloud model disaster recovery services are no longer suitable for the current complex cloud storage systems. Therefore, a scheme to provide disaster recovery for cloud storage services in a multi-cloud storage environment is needed in real production. In this paper, we propose a disaster recovery scheme based on blockchain technology. The proposed scheme outlined in this study aims to address the issue of data availability within the cloud storage landscape. The proposed scheme achieves this goal by dividing data into hot and cold categories, verifying the integrity of copy data via blockchain technology, and utilizing blockchain networks to manage multi-cloud storage systems. Experimental findings demonstrate that the proposed scheme yields superior results in terms of computation and time overheads. © 2023 IEEE.

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