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arXiv 2021年

作者： Dudkina, Ekaterina Bin, Michelangelo Breen, Jane Crisostomi, Emanuele Ferraro, Pietro Kirkland, Steve Marecek, Jakub Murray-Smith, Roderick Parisini, Thomas Stone, Lewi Yilmaz, Serife Shorten, Robert Department of Energy Systems Territory and Constructions Engineering University of Pisa Pisa Italy Department of Electrical and Electronic Engineering Imperial College London London United Kingdom Ontario Tech University Canada Dyson School of Design Engineering Imperial College London London United Kingdom Department of Mathematics University of Manitoba Canada Czech Technical University Prague Czech Republic School of Computing Science University of Glasgow Glasgow United Kingdom Department of Engineering and Architecture University of Trieste Trieste Italy KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus The George S. Wise Faculty of Life Sciences Tel Aviv University Israel Mathematics School of Science RMIT University Melbourne Australia

The ranking of nodes in a network according to their "importance" is a classic problem that has attracted the interest of different scientific communities in the last decades. The current COVID-19 pandemic has recently rejuvenated the interest in this problem, as it is related to the selection of which individuals should be tested in a population of asymptomatic individuals, or which individuals should be vaccinated first. Motivated by the COVID-19 spreading dynamics, in this paper we review the most popular methods for node ranking in undirected unweighted graphs, and compare their performance in a benchmark realistic network, that takes into account the community-based structure of society. Also, we generalize a classic benchmark network originally proposed by Newman for ranking nodes in unweighted graphs, to show how ranks change in the weighted case. Copyright © 2021, The Authors. All rights reserved.

关键词： Benchmarking

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Application of Improved GM(1,1) Models in Seasonal Monthly Tourism Demand Forecast

Application of Improved GM(1,1) Models in Seasonal Monthly T...

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International Conference on Artificial Intelligence and Data sciences (AiDAS)

作者： Ani Shabri Ruhaidah Samsudin School of Mathematic Faculty of Science Universiti Teknologi Malaysia Skudai School of Computing Faculty of Engineering Universiti Teknologi Malaysia Skudai Johor

ISBN: (纸本)9781728130422

The majority of tourism demand time series show patterns in terms of seasonal, cyclical and trend components, leading to low accuracy in medium and long-term data forecasting. In order to solve this problem, this paper presents an improved grey model (IGM) based on a re-shaped time series and a genetically optimized method. The monthly arrivals of tourists to Langkawi Island in Malaysia between January 2004 and December 2016 were used to verify the efficiency of the optimized model in anticipating the demand for tourism. The results show that the proposed model achieves better forecasting accuracy on the data with increasing trend, seasonal and cyclical patterns.

关键词： Background value Boundary value Electricity demand forecasting Grey model Optimization load forecasting Boundary values tourism Demand Prediction tourists Monthly seasonal

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Nucleus region detection of cervical cytology using image channel conversion techniques

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AIP Conference Proceedings 2024年第1期3135卷

作者： Hiam Alquran Alaa Badarneh Mohammed Alsalatie Wan Azani Mustafa Batul Alquraan Department of Biomedical Systems and Medical Informatics Engineering Yarmouk University Irbid Jordan Department of Biomedical Engineering Jordan University of Science and Technology Irbid Jordan The Institute of Biomedical Technology King Hussein Medical Center Royal Jordanian Medical Service Amman 11855 Jordan Faculty of Electrical Engineering & Technology University of Malaysia Perlis UniCITI Alam Campus Sungai Chuchuh Perlis Malaysia Advanced Computing (AdvComp) Centre of Excellence (CoE) Universiti Malaysia Perlis (UniMAP) Campus Pauh Putra 02600 Arau Perlis Malaysia

Cervical cancer is a form of cancer that develops in the cells of the cervix, which links the uterus to the vagina. Cervical cancer can often be prevented by attending the cervical screening, which aims to find and treat changes to cells before they turn into cancer. Pap smear images are a more popular technique for screening cervical cancer. The nucleus and cytoplasm are present in the smear cell images. The spread of cancer is determined by the shape and structure of the nucleus. Thus, nucleus segmentation is an important step in cancer detection. However, overlapping, poor contrast, uneven staining, and other factors make cervical nucleus segmentation difficult. This paper proposes a new segmentation method for the cervical nucleus using digital image processing. In our proposed method, image processing techniques are employed to segment the nucleus. This method mainly compared the performance of using Red Green Blue (RGB) channels and Hue, Saturation, and Value (HSV). The best two channels from each two-colored image representation are compared, where the V channel is the best among H and S channels. On the other hand, G chancel is the best among the G and B channels. Therefore, the best two channels are compared, and the segmentation using the G channel is the best among all, with a sensitivity of 94.3%, specificity of 94.6% and precision of 88.3%. The main impact of this paper will be to assist doctors in diagnosing cervical cancer-based segmentation of the nucleus in Pap smear images.

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From QoS Distributions to QoE Distributions: a System's Perspective

From QoS Distributions to QoE Distributions: a System's Pers...

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IEEE Conference on Network Softwarization (NetSoft)

作者： Tobias Hoßfeld Poul E. Heegaard Martín Varela Lea Skorin-Kapov Markus Fiedler Chair of Communication Networks University of Würzburg Germany NTNU Norwegian University of Science and Technology Trondheim Norway Independent Researcher Oulu Finland Faculty of Electrical Engineering and Computing University of Zagreb Croatia Blekinge Institute of Technology Karlshamn Sweden

ISBN: (数字)9781728156842

ISBN: (纸本)9781728156859

In the context of QoE management, network and service providers commonly rely on models that map system QoS conditions (e.g., system response time, paket loss, etc.) to estimated end user QoE values. Observable QoS conditions in the system may be assumed to follow a certain distribution, meaning that different end users will experience different conditions. On the other hand, drawing from the results of subjective user studies, we know that user diversity leads to distributions of user scores for any given test conditions (in this case referring to the QoS parameters of interest). Our previous studies have shown that to correctly derive various QoE metrics (e.g., Mean Opinion Score (MOS), quantiles, probability of users rating “good or better”, etc.) in a system under given conditions, there is a need to consider rating distributions obtained from user studies, which are often times not available. In this paper we extend these findings to show how to approximate user rating distributions given a QoS-to-MOS mapping function and second order statistics. Such a user rating distribution may then be combined with a QoS distribution observed in a system to finally derive corresponding distributions of QoE scores. We provide two examples to illustrate this process: 1) analytical results using a Web QoE model relating waiting times to QoE, and 2) numerical results using measurements relating packet losses to video stall pattern, which are in turn mapped to QoE estimates.

关键词： Quality of experience Quality of service Measurement System performance Standards Probability density function Random variables

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Expression of Concern for: Improved ML Models on Feature Extraction in HSI

Expression of Concern for: Improved ML Models on Feature Ext...

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computing and Information Technology (ICCIT-1441), 2020 International Conference on

作者： C. Narmatha M. Sivaram P. Manimegalai Ravi Samikannu Pallavi Murghai Goel Juveriya Khan Department of Computer Engineering Faculty of Computers and Information Technology University of Tabuk Saudi Arabia Department of Computer Networking Lebanese French University Erbil Kurdistan Region Iraq Biomedical Engineering Department Karunya Institute of Technology and Sciences Karunya Nagar India Faculty of Electrical Engineering Botswana International University of Science and Technology Botswana School of Computing Science and Engineering Galgotias University Greater Noida India Department of Information Technology Emirates College for Management and Information Technology United Arab Emirates

ISBN: (数字)9781728126807

ISBN: (纸本)9781728126814

Hyper Spectral Image (HSI) is widely used in remote sensing applications that take into account thousands of spectral channel compositions over a single scene. HSI requires accurate models of learning to extract the features of the HSI. Due to the presence of its spatial and spectral resolution, the image learning model presents a core challenge due to its complicated nature of image frames. In order to assist it during the learning process, several attempts have been made to address its complicated nature. These methods, however, failed to provide HSI understanding. Using ML techniques addresses the issues due to the presence of mixed pixels, large amounts of data and limited samples of training. The process of ML addresses the complex relationship between image data. We present various methods of ML used to learn HSI in this paper. Initially, for different image processing techniques, we present an overview of different ML methods. A system review is then performed on the basis of deep learning based on various HSI learning models. Finally, for future study, we are discussing the challenges and possible directions.

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The Implementation of NetFlow Log System Using Ceph and ELK Stack 6th

The Implementation of NetFlow Log System Using Ceph and ELK ...

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6th International Conference on Frontier computing, FC 2018

作者： Wang, Yuan-Ting Yang, Chao-Tung Kristiani, Endah Liu, Ming-Lun Lai, Ching-Han Jiang, Wei-Je Chan, Yu-Wei Department of Computer Science Tunghai University Taichung City40704 Taiwan Department of Industrial Engineering and Enterprise Information Tunghai University Taichung City40704 Taiwan Department of Informatics Faculty of Engineering and Computer Science Krida Wacana Christian University Jakarta11470 Indonesia College of Computing and Informatics Providence University Taichung City43301 Taiwan

ISBN: (纸本)9789811336478

Each software always has log data. Log data is essential for developers and end-users. With log data, we can indicate the information of system error, warning events or whether our system processes are loading successfully. This paper aims to provide a virtualization platform for our campus NetFlow log data in order user can use for further analysis. Our system used Ceph storage system and ELK Stack technology. Ceph provides a vast amount of scalable storage and ELK consists of three components, namely: Elasticsearch, Logstash, and Kibana. Elasticsearch is a search engine in which can be used for searching all kinds of documents. Logstash is used for managing centralize, transform and stash our data to Elasticsearch and Kibana is used for visualizing Elasticsearch data and an excellent tool for a real-time data analytics and decision making. Our data is continuous data. Approximately our data growth up 38 GB per day and our data have grown more than 8TB until now. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Decision making

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Correction: Ambient Assisted Living: Scoping Review of Artificial Intelligence Models, Domains, Technology, and Concerns

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Journal of medical Internet research 2022年第12期24卷 e45081页

作者： Mladjan Jovanovic Goran Mitrov Eftim Zdravevski Petre Lameski Sara Colantonio Martin Kampel Hilda Tellioglu Francisco Florez-Revuelta Department of Computer Science Singidunum University Belgrade Serbia. Faculty of Computer Science and Engineering University Saints Cyril and Methodius Skopje North Macedonia. Signals & Images Lab Institute of Information Science and Technologies National Research Council of Italy Pisa Italy. Faculty of Informatics Vienna University of Technology Vienna Austria. Department of Computing Technology University of Alicante Alicante Spain.

[This corrects the article DOI: 10.2196/36553.].

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Corrigendum to “Review of artificial neural networks-contribution methods integrated with structural equation modeling and multi-criteria decision analysis for selection customization” [Eng. Appl. Artif. Intell. 124 (2023) 106643]

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engineering Applications of Artificial Intelligence 2024年 128卷

作者： A.A. Zaidan Alhamzah Alnoor O.S. Albahri R.T. Mohammed A.H. Alamoodi A.S. Albahri B.B. Zaidan Salem Garfan Hamsa Hameed Mohammed S. Al-Samarraay Ali Najm Jasim R.Q. Malik SP Jain School of Global Management Sydney Australia Management Technical College Southern Technical University Basrah Iraq Department of Computer Science and Information Technology La Trobe University Melbourne VIC Australia Computer Techniques Engineering Department Mazaya University College Thi-Qar Nassiriya Iraq Department of Computing Science Faculty of Science Komar University of Science and Technology (KUST) Sulaymaniyah Iraq Department of Computing and META Technology (FKMT) Universiti Pendidikan Sultan Idris Tanjung Malim 35900 Malaysia MEU Research Unit Middle East University Amman Jordan Iraqi Commission for Computers and Informatics (ICCI) Baghdad Iraq Technical College Imam Ja'afar Al-Sadiq University Baghdad Iraq Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin 64002 Taiwan Faculty of Human Development Sultan Idris University of Education (UPSI) Tanjung Malim Malaysia Foundation of Alshuhda Baghdad Iraq Medical Instrumentation Techniques Engineering Department Al-Mustaqbal University College Babylon Iraq

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The Implementation of Wi-Fi Log Analysis System with ELK Stack 6th

The Implementation of Wi-Fi Log Analysis System with ELK Sta...

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6th International Conference on Frontier computing, FC 2018

作者： Wang, Yuan-Ting Yang, Chao-Tung Kristiani, Endah Chan, Yu-Wei Department of Computer Science Tunghai University Taichung City40704 Taiwan Department of Industrial Engineering and Enterprise Information Tunghai University Taichung City40704 Taiwan Department of Informatics Faculty of Engineering and Computer Science Krida Wacana Christian University Jakarta11470 Indonesia College of Computing and Informatics Providence University Taichung City43301 Taiwan

ISBN: (纸本)9789811336478

Log data is crucial for developers, decision maker, and end-users. With log data, we can inspect whether the system error or not, give warning events or whether our system processes are loFinally, the values in this tableading successfully or not. This paper aims to provide a virtualization platform for our campus Wi-Fi log data for further analysis. Our system used ELK Stack technology. It consists of three components: Elasticsearch, Logstash, and Kibana. Elasticsearch is a search engine which used for searching all kinds of documents, Logstash used for managing centralize, transform and stash our data to Elasticsearch, and Kibana applied for visualizing Elasticsearch data and an excellent tool for a real-time data analytics and decision making. Our data is a Netflow traffic data. Approximately our data growth up 10 MB per day and our data have grown to 19 GB until now. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Search engines

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Data quality control system and long-term performance monitor of LHAASO-KM2A

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Astroparticle Physics 2025年 164卷

作者： Cao, Zhen Aharonian, F. Axikegu Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Bian, W. Bukevich, A.V. Cao, Q. Cao, W.Y. Cao, Zhe Chang, J. Chang, J.F. Chen, A.M. Chen, E.S. Chen, H.X. Chen, Liang Chen, Lin Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, Y. Cheng, N. Cheng, Y.D. Cui, M.Y. Cui, S.W. Cui, X.H. Cui, Y.D. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu Dong, X.Q. Duan, K.K. Fan, J.H. Fan, Y.Z. Fang, J. Fang, J.H. Fang, K. Feng, C.F. Feng, H. Feng, L. Feng, S.H. Feng, X.T. Feng, Y. Feng, Y.L. Gabici, S. Gao, B. Gao, C.D. Gao, Q. Gao, W. Gao, W.K. Ge, M.M. Geng, L.S. Giacinti, G. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. Hasan, M. He, H.H. He, H.N. He, J.Y. He, Y. Hor, Y.K. Hou, B.W. Hou, C. Hou, X. Hu, H.B. Hu, Q. Hu, S.C. Huang, D.H. Huang, T.Q. Huang, W.J. Huang, X.T. Huang, X.Y. Huang, Y. Ji, X.L. Jia, H.Y. Jia, K. Jiang, K. Jiang, X.W. Jiang, Z.J. Jin, M. Kang, M.M. Karpikov, I. Kuleshov, D. Kurinov, K. Li, B.B. Li, C.M. Li, Cheng Li, Cong Li, D. Li, F. Li, H.B. Li, H.C. Li, Jian Li, Jie Li, K. Li, S.D. Li, W.L. Li, X.R. Li, Xin Li, Y.Z. Li, Zhe Li, Zhuo Liang, E.W. Liang, Y.F. Lin, S.J. Liu, B. Liu, C. Liu, D. Liu, D.B. Liu, H. Liu, H.D. Liu, J. Liu, J.L. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y. Liu, Y.N. Luo, Q. Luo, Y. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Min, Z. Mitthumsiri, W. Mu, H.J. Nan, Y.C. Neronov, A. Ou, L.J. Pattarakijwanich, P. Pei, Z.Y. Qi, J.C. Qi, M.Y. Qiao, B.Q. Qin, J.J. Raza, A. Ruffolo, D. Sáiz, A. Saeed, M. Semikoz, D. Shao, L. Shchegolev, O. Sheng, X.D. Shu, F.W. Song, H.C. Stenkin, Yu.V. Stepanov, V. Su, Y. Sun, D.X. Sun, Q.N. Sun, X.N. Sun, Z.B. Key Laboratory of Particle Astrophysics & Experimental Physics Division & Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Beijing100049 China TIANFU Cosmic Ray Research Center Sichuan Chengdu China Dublin Institute for Advanced Studies 31 Fitzwilliam Place 2 Dublin Ireland Max–Planck-Institut for Nuclear Physics P.O. Box 103980 Heidelberg69029 Germany School of Physical Science and Technology & School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu610031 China School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China Tsung-Dao Lee Institute & School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China Institute for Nuclear Research of Russian Academy of Sciences Moscow117312 Russia Hebei Normal University Hebei Shijiazhuang050024 China University of Science and Technology of China Anhui Hefei230026 China State Key Laboratory of Particle Detection and Electronics China Key Laboratory of Dark Matter and Space Astronomy & Key Laboratory of Radio Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Research Center for Astronomical Computing Zhejiang Laboratory Zhejiang Hangzhou311121 China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China Ministry of Education Tibet Lhasa850000 China Key Laboratory of Radio Astronomy and Technology National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Sun Yat-sen University Guangdong Zhuhai519000 China Sun Yat-sen University Guangdong Guangzhou510275 China Sun Yat-sen University Guangdong Zhuhai519000 Chi

The KM2A is the largest sub-array of the Large High Altitude Air Shower Observatory (LHAASO). It consists of 5216 electromagnetic particle detectors (EDs) and 1188 muon detectors (MDs). The data recorded by the EDs and MDs are used to reconstruct primary information of cosmic-ray and gamma-ray showers. To ensure the reliability of the LHAASO-KM2A data, a three-level quality control system has been established. It is used to monitor the status of detector units, stability of reconstructed parameters and the performance of the array based on observations of the Crab Nebula and Moon shadow. This paper will introduce the control system and its application on the LHAASO-KM2A data collected from August 2021 to July 2023. During this period, the pointing and angular resolution of the array were stable. From the observations of the Moon shadow and Crab Nebula, the results achieved using the two methods are consistent with each other. For example, according to the observation of the Crab Nebula with KM2A at energies from 25 TeV to 100 TeV, the time averaged pointing errors are estimated to be −0.003°±0.005° and 0.001°±0.006° in the R.A. and Dec directions, respectively. © 2024 Elsevier B.V.

关键词： Cosmology

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