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Rank the spreading influence of nodes using dynamic Markov process

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arXiv 2022年

作者： Lin, Jian-Hong Yang, Zhao Liu, Jian-Guo Chen, Bo-Lun Tessone, Claudio J. Blockchain and Distributed Ledger Technologies Institute of Informatics University of Zürich Andreasstrasse 15 ZürichCH-8050 Switzerland UZH Blockchain Center University of Zürich Andreasstrasse 15 ZürichCH-8050 Switzerland ETH Zürich Department of Management Technology and Economics Scheuchzerstrasse 7 ZürichCH-8092 Switzerland Global People Analytics Talent Rewards & Insights Roche BaselCH-4070 Switzerland Institute of Accounting and Finance Shanghai University of Finance and Economics Shanghai200433 China Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huaian233003 China Swiss Center for Data and Network Sciences University of Zurich ZürichCH-8050 Switzerland

Ranking the spreading influence of nodes is of great importance in practice and research. The key to ranking a node's spreading ability is to evaluate the fraction of susceptible nodes been infected by the target node during the outbreak, i.e., the outbreak size. In this paper, we present a dynamic Markov process (DMP) method by integrating the Markov chain and the spreading process to evaluate the outbreak size of the initial spreader. Following the idea of the Markov process, this method solves the problem of nonlinear coupling by adjusting the state transition matrix and evaluating the probability of the susceptible node being infected by its infected neighbours. We have employed the susceptible-infected-recovered (SIR) and susceptible-infected-susceptible (SIS) models to test this method on real-world static and temporal networks. Our results indicate that the DMP method could evaluate the nodes' outbreak sizes more accurately than previous methods for both single and multi-spreaders. Besides, it can also be employed to rank the influence of nodes accurately during the spreading process. Copyright © 2022, The Authors. All rights reserved.

关键词： Markov processes

A comparison of deep learning-based techniques for solving partial differential equations

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AIP Conference Proceedings 2024年第1期2905卷

作者： Rabiu Bashir Yunus Nooraini Zainuddin Afza Shafie Muhammad Izzatullah Samsul Ariffin Abdul Karim Fundamental and Applied Sciences Department Universiti Teknologi PETRONAS Bandar Seri Iskandar 32610 Seri Iskandar Perak Darul Ridzuan Malaysia King Abdullah University of Science and Technology (KAUST) Division of Physical Sciences and Engineering Bldg. 1 Desk #0202-WS023 Thuwal 23955-6900 Kingdom of Saudi Arabia Software Engineering Programme Faculty of Computing and Informatics Universiti Malaysia Sabah Jalan UMS 88400 Kota Kinabalu Sabah Malaysia Data Technologies and Applications (DaTA) Research Group Universiti Malaysia Sabah Jalan UMS 88400 Kota Kinabalu Sabah Malaysia

Obtaining the solutions of high-dimensional partial differential equations (PDEs) seems to be difficult by utilizing the classical numerical methods. Recently, deep neural networks (DNNs) techniques have received special attentions in solving high–dimensional problems in PDEs. In this study, our quest is to investigate some newly introduced data-driven deep learning-based approaches and compare their performance in terms of their efficiency and faster training towards high-dimensional PDEs. However, the comparison is carried out based on different activation functions, number of layers and gradient based optimizers. We consider some benchmark problems in our numerical experiments which includes Burgers equation, Diffusion-reaction equation and Allen-Cahn Equations.

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A Novel Lightweight Particle Filter for Indoor Localization

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A Novel Lightweight Particle Filter for Indoor Localization

International Conference on Indoor Positioning and Indoor Navigation (IPIN)

作者： Georgios Pipelidis Nikolaos Tsiamitros Christian Gentner Dina Bousdar Ahmed Christian Prehofer Faculty of Informatics Research Group for Software & Systems Engineering Technical University of Munich Munich Germany German Aerospace Center Institute of Communication and Navigation Oberpfaffenhofen-Wessling Germany

In this paper, we describe an infrastructure-independent indoor localization approach for various indoor environments. Our method introduces a novel particle filter implementation that enables the fusion of inertial motion unit sensors, user context, user gait direction, and map information. Due to this novel fusion, it performs localization with up to two orders of magnitude fewer particles than state-of-the-art approaches. Additionally, it extracts map information via existing open services, such as the Open Street Maps and it follows defined standards for the map handling. We evaluated all the components of our method in realtime in off-the-shelf smartphones and we find that it performs a median error of 2.3m, while using only 40 particles instead of 400 or up to 4000 particles that other methods require for the same accuracy.

关键词： Estimation Microsoft Windows Accelerometers Smart phones Uncertainty Gravity Indoor navigation

Optimization of Neural Network Parameters Based on a Genetic Algorithm for Prediction of Time Series

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Optimization of Neural Network Parameters Based on a Genetic...

International Multi-Conference on Industrial engineering and Modern Technologies (FarEastCon)

作者： V.S. Tormozov A.L. Zolkin K.A. Vasilenko Senior lecturer of the Software engineering for computers and computer-based systems sub-faculty Pacific National University Khabarovsk Tikhookeanskaya St. 136 Ph.D. in Engineering Science Povolzhskiy State University of Telecommunications and Informatics Samara Highest category lecturer Service and design college of the Vladivostok State University of Economics and Service Service and design college of the Vladivostok State University of Economics and Service Vladivostok

ISBN: (数字)9781728169514

ISBN: (纸本)9781728169521

The article proposes a method that allows to solve the complex combinatorial problem of structural optimization of an artificial neural network with a large dimension of the space of optimization parameters using the stochastic mutation function with a constant coefficient of variation. A study on assessment of applicability of this approach to solve the problem is given. This method is proposed to be used for the forecasting of time series values.

关键词： Genetic algorithms Sociology Neurons Biological cells Artificial neural networks Time series analysis Optimization

A cyber-enabled mission-critical system for post-flood response: Exploiting TV white space as network backhaul links

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A cyber-enabled mission-critical system for post-flood respo...

作者： Rahman, M.D. Arafatur Asyhari, A. Taufiq Azad, Saiful Hasan, M.D. Munirul Munaiseche, Cindy P.C. Krisnanda, Made Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Kuantan26300 Malaysia IBM Center of Excellence and Earth Resources and Sustainability Center Universiti Malaysia Pahang Kuantan26300 Malaysia School of Computing Electronics and Mathematics Coventry University CoventryCV1 5FB United Kingdom Telkom University Jalan Telekomunikasi Terusan Buah Batu Bandung40257 Indonesia Faculty of Engineering Department of Informatics Universitas Negeri Manado Tondano95618 Indonesia Faculty of Engineering Department of Information and Communication Technology Education Universitas Negeri Manado Tondano95618 Indonesia

A crucial problem in post-flood recovery actions is the ability to rapidly establish communication and collaboration among rescuers to conduct timely and effective search and rescue (SAR) mission given disrupted telecommunication infrastructure to support the service. Aimed at providing such proximity service (ProSe) for mission-critical data exchange in the post-flood environment, the majority of existing solutions rely heavily upon ad-hoc networking approaches, which suffer from restricted communication range and the limited scope of interaction. As an effort to broaden the ProSe coverage and expand integrated global-local information exchange in the post-flood SAR activities, this paper proposes a novel network architecture in the form of a cyber-enabled mission-critical system (CEMCS) for acquiring and communicating post-flood emergency data by exploiting TV white space spectrum as network backhaul links. The primary method of developing the proposed system builds upon a layered architecture of wireless local, regional and wide-area communications, and incorporates collaborative network components among these layers. The desirable functionalities of CEMCS are showcased through formulation and the development of an efficient global search strategy exploiting a wide range of collaboration among network agents. The simulation results demonstrate the capability of CEMCS to provide ProSe in the post-flood scenarios as reflected by reliable network performance (e.g., packet delivery ratio nearing 80%–90%) and the optimality of efficient search algorithm. © 2017 Oxford University Press. All rights reserved.

关键词： Cyber Physical System

AI-based Fog and Edge Computing: A Systematic Review, Taxonomy and Future Directions

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arXiv 2022年

作者： Iftikhar, Sundas Gill, Sukhpal Singh Song, Chenghao Xu, Minxian Aslanpour, Mohammad Sadegh Toosi, Adel N. Du, Junhui Wu, Huaming Ghosh, Shreya Chowdhury, Deepraj Golec, Muhammed Kumar, Mohit Abdelmoniem, Ahmed M. Cuadrado, Felix Varghese, Blesson Rana, Omer Dustdar, Schahram Uhlig, Steve School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom University of Kotli Azad Jammu & Kashmir Azad Kashmir Kotli Pakistan Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Software Systems and Cybersecurity Faculty of Information Technology Monash University Australia Csiro DATA61 Australia Center for Applied Mathematics Tianjin University Tianjin China The Pennsylvania State University PA United States Naya Raipur India Abdullah Gül University Kayseri Turkey Department of Information Technology National Institute of Technology Jalandhar India Spain School of Computer Science University of St Andrews United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria

Resource management in computing is a very challenging problem that involves making sequential decisions. Resource limitations, resource heterogeneity, dynamic and diverse nature of workload, and the unpredictability of fog/edge computing environments have made resource management even more challenging to be considered in the fog landscape. Recently Artificial Intelligence (AI) and Machine Learning (ML) based solutions are adopted to solve this problem. AI/ML methods with the capability to make sequential decisions like reinforcement learning seem most promising for these type of problems. But these algorithms come with their own challenges such as high variance, explainability, and online training. The continuously changing fog/edge environment dynamics require solutions that learn online, adopting changing computing environment. In this paper, we used standard review methodology to conduct this Systematic Literature Review (SLR) to analyze the role of AI/ML algorithms and the challenges in the applicability of these algorithms for resource management in fog/edge computing environments. Further, various machine learning, deep learning and reinforcement learning techniques for edge AI management have been discussed. Furthermore, we have presented the background and current status of AI/ML-based Fog/Edge Computing. Moreover, a taxonomy of AI/ML-based resource management techniques for fog/edge computing has been proposed and compared the existing techniques based on the proposed taxonomy. Finally, open challenges and promising future research directions have been identified and discussed in the area of AI/ML-based fog/edge computing. Copyright © 2022, The Authors. All rights reserved.

关键词： Edge computing

An improved simulation model for pedestrian crowd evacuation

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TechRxiv

TechRxiv 2020年

作者： Muhammed, Danial A. Rashid, Tarik A. Alsadoon, Abeer Bacanin, Nebojsa Fattah, Polla Mohammadi, Mokhtar Banerjee, Indradip Computer Department College of Science University of Sulaimani KRG Sulaymaniyah46001 Iraq Computer Science and Engineering Department University of Kurdistan Hewler KRG Erbil44001 Iraq Department of Information Technology School of Computing and Mathematics Charles Sturt University Bathurst SydneyNSW2795 Australia Faculty of Informatics and Computing Vice-dean Singidunum University Belgrade11000 Serbia Software and Informatics Engineering Dept Engineering College Salahaddin University-Erbil KRG Erbil44001 Iraq Department of Information Technology Lebanese French University KRG Erbil44001 Iraq Department of Computer Science and Engineering University Institute of Technology The University of Burdwan West Bengal Burdwan713101 India

This paper works on one of the most recent pedestrian crowd evacuation models, i.e., "a simulation model for pedestrian crowd evacuation based on various AI techniques", developed in late 2019. This study adds a new feature to the developed model by proposing a new method and integrating it with the model. This method enables the developed model to find a more appropriate evacuation area design, among others regarding safety due to selecting the best exit door location among many suggested locations. This method is completely dependent on the selected model's output, i.e., the evacuation time for each individual within the evacuation process. The new method finds an average of the evacuees’ evacuation times of each exit door location;then, based on the average evacuation time, it decides which exit door location would be the best exit door to be used for evacuation by the evacuees. To validate the method, various designs for the evacuation area with various written scenarios were used. The results showed that the model with this new method could predict a proper exit door location among many suggested locations. Lastly, from the results of this research using the integration of this newly proposed method, a new capability for the selected model in terms of safety allowed the right decision in selecting the finest design for the evacuation area among other designs. © 2020, CC BY.

关键词： Location

An improved simulation model for pedestrian crowd evacuation

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arXiv 2020年

作者： Muhammed, Danial A. Rashid, Tarik A. Alsadoon, Abeer Bacanin, Nebojsa Fattah, Polla Mohammadi, Mokhtar Banerjee, Indradip Computer Department College of Science University of Sulaimani SulaymaniyahKRG46001 Iraq Computer Science and Engineering Department University of Kurdistan Hewler ErbilKRG44001 Iraq Department of Information Technology School of Computing and Mathematics Charles Sturt University Sydney BathurstNSW2795 Australia Faculty of Informatics and Computing Vice-dean Singidunum University Belgrade11000 Serbia Software and Informatics Engineering Dept Engineering College Salahaddin University-Erbil ErbilKRG44001 Iraq Department of Information Technology Lebanese French University ErbilKRG44001 Iraq Department of Computer Science and Engineering University Institute of Technology The University of Burdwan BurdwanWest Bengal713101 India

关键词： Location

A multi hidden recurrent neural network with a modified grey wolf optimizer

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arXiv 2019年

作者： Rashid, Tarik A. Abbas, Dosti K. Turel, Yalin K. Computer Science and Engineering Department University of Kurdistan Hewler Kurdistan Iraq Software and Informatics Engineering Salahaddin University-Erbil Kurdistan Iraq Faculty of Engineering Soran University Kurdistan Iraq Department of Computer Education and Instructional Technology Firat University Elazig Turkey

Identifying university students' weaknesses results in better learning and can function as an early warning system to enable students to improve. However, the satisfaction level of existing systems is not promising. New and dynamic hybrid systems are needed to imitate this mechanism. A hybrid system (a modified Recurrent Neural Network with an adapted Grey Wolf Optimizer) is used to forecast students' outcomes. This proposed system would improve instruction by the faculty and enhance the students' learning experiences. The results show that a modified recurrent neural network with an adapted Grey Wolf Optimizer has the best accuracy when compared with other models. Copyright © 2019, The Authors. All rights reserved.

关键词： Recurrent neural networks