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6th Smart Cities Symposium (SCS 2022)

作者： A. Matuka S. S. Asafo G. O. Eweke P. Mishra S. Ray M. Abotaleb T. Makarovskikh A.A. Alhussan A.A. Abdelhamid E.M. El-Kenawy P. Dutta S. Chowdhury University of Macerata Department of Economics and Law Macerata Italy Department of Banking & Finance Niger Delta University Amassoma Nigeria College of Agriculture J.N.K.V.V. Powarkheda India Centurion University of Technology and Management Paralakhemundi India Department of System Programming South Ural State University Chelyabinsk Russia Department of Computer Sciences College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia Department of Computer Science Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Department of Communications and Electronics Delta Higher Institute of Engineering and Technolog School of Engineering and Technology Amity University Kolkata Kolkata India MCA Department Sri Venkateshwara College of Engineering and Technology Chittoor India

The covid-19 pandemic and Economic Policy Uncertainty resulting from the shutdown of production, withdrawal of investments, enforcement of lockdowns and quarantines globally, have been directly affecting stock markets worldwide. This study is thus an attempt to analyse the impact of the COVID-19 pandemic on stock market behaviour in major affected economies. Moreover, the time frame was extended by using current data which investigate the impact of the virus during the boom and the blast phase in the country's most hit by the pandemic crisis such as China, Italy, UK and US. The frequency of the data is daily, and it dates from 3 January 2020 up to 10 February 2021. The considered time framework will give a deep insight into how stock markets behave in the case of an exogenous shock. The Dickey-Fuller Augmented Unit Root Test indicates that all the variables are stationary at first difference, which is one of the main conditions to have robust result, and the ARCH-LM test for the heteroskedasticity of the residuals, which show that all the probability values are significant, rejecting the null hypothesis of no ARCH effect. Based on the results of GARCH (1,1), we conclude that the change in stock markets volatility is positive and significant in China, Italy, UK and USA. This suggests that the impacts of COVID-19 outbreak and economic policy uncertainty on Stock Markets are a significant and Homogeneous across the studied countries, and the shock on FTSE Italia All Share has the longest time to vanish which makes it the riskiest to invest during this period, while the shock on SP500 USA has the shortest time to disappear meaning that it is safest Stock Market in this study. These findings have significant implications for policymakers, institutional and individual investors and Financial Markets analysts.

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The Regularization of Small Sub-Constraint Satisfaction Problems 22nd

The Regularization of Small Sub-Constraint Satisfaction Prob...

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22nd International Conference on Applications of Declarative programming and Knowledge Management, INAP 2019, the 33rd Workshop on Logic programming, WLP 2019, and the 27th Workshop on Functional and Constraint Logic programming, WFLP 2019

作者： Löffler, Sven Liu, Ke Hofstedt, Petra Department of Mathematics and Computer Science MINT Programming Languages and Compiler Construction Group Brandenburg University of Technology Cottbus-Senftenberg Konrad-Wachsmann-Allee 5 Cottbus03044 Germany

ISBN: (纸本)9783030467135

This paper describes a new approach on optimization of constraint satisfaction problems (CSPs) by means of substituting sub-CSPs with locally consistent regular membership constraints. The purpose of this approach is to reduce the number of fails in the resolution process, to improve the inferences made during search by the constraint solver by strengthening constraint propagation, and to maintain the level of propagation while reducing the cost of propagating the constraints. Our experimental results show improvements in terms of the resolution speed compared to the original CSPs and a competitiveness to the recent tabulation approach [1, 15]. Besides, our approach can be realized in a preprocessing step, and therefore wouldn’t collide with redundancy constraints or parallel computing if implemented. © 2020, Springer Nature Switzerland AG.

关键词： Constraint satisfaction problems

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Solution of the Retail Marketing Problem of Rational Choice of the Location of Trade Enterprises Using the Method of Hierarchy Analysis and Fuzzy Set Theory 1

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14th International Conference on Applications of Fuzzy Systems, Soft Computing, and Artificial Intelligence Tools, ICAFS 2020

作者： Veliyev, Ahmed Abdullayev, Tarlan Alekperov, Ramiz Salahli, Vuqar Odlar Yurdu Univercity BakuAZ1072 Azerbaijan Scientific Affairs Odlar Yurdu Univercity BakuAZ1072 Azerbaijan Informathion Technology and Programming Azerimed LLC BakuAZ1147 Azerbaijan Department of Computer Engineering Odlar Yurdu Univercity BakuAZ1072 Azerbaijan

ISBN: (数字)9783030640583

ISBN: (纸本)9783030640576

The principles of solving the multicriteria problem of retail marketing for the rational choice of the location of trade enterprises (alternatives) based on the classical method of analysis of Saaty hierarchies, using the theory of fuzzy sets, are considered. The choice of the best alternative is carried out on the basis of the arguments of the maxima (abbreviated arg max), as the arguments, which are evaluated based on the operation of intersection of fuzzy sets of criteria. This approach is very practical from the point of view of using the experience and intuition of marketing workers and requires minimal costs to solve similar problems. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Fuzzy set theory

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Prediction and graph visualization of cyber attacks using graph attention networks

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computers & Security 2025年 157卷

作者： Mucahit Soylu Resul Das Inonu University Department of Organized Industrial Zone Vocational School Computer Programming Malatya Turkiye Firat University Faculty of Technology Department of Software Engineering 23119 Elazig Turkiye

This study proposes a hybrid approach for visualizing cyberattacks by combining the deep learning-based GAT model with JavaScript-based graph visualization tools. The model processes large, heterogeneous data from the UNSW-NB15 dataset to generate dynamic and meaningful graphs. In the data cleaning phase, missing and erroneous data were removed, unnecessary columns were discarded, and the data was transformed into a format suitable for modeling. Then, the data was converted into homogeneous graphs, and heterogeneous structures were created for analysis using the GAT model. GAT prioritizes relationships between nodes in the graph with an attention mechanism, effectively detecting attack patterns. The analyzed data was then converted into interactive graphs using tools like SigmaJS, with attacks between the same nodes grouped to reduce graph complexity. Users can explore these dynamic graphs in detail, examine attack types, and track events over time. This approach significantly benefits cybersecurity professionals, allowing them to better understand, track, and develop defense strategies against cyberattacks.

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Design of Intelligent Decision Support System With Ensembled Machine Learning to Predict Students' Performance

Design of Intelligent Decision Support System With Ensembled...

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Communication, computer Sciences and Engineering (IC3SE), International Conference on

作者： S Deepan Dineshkumar Rajendran Navdeep Dhaliwal K Praveena Melanie Lourens Anandhi R J School of CS & AI SR University Warangal Telangana India Computer Games Design and Programming School of Computing and Innovative Technologies British University Vietnam Vietnam Lovely Professional University Phagwara India Institute of Aeronautical Engineering Dundigal Hyderabad Deputy Dean Faculty of Management Sciences Durban University of Technology South Africa Department of Information Science Engineering New Horizon College of Engineering Bangalore

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

Evaluating student performance is important for universities and institutions in the current student education landscape because it helps them create models that work better for students. The automation of various features related to fundamental student traits and behaviours that manage massive amounts of data efficiently processes these. To handle student records that included information about students' behaviour and how it related to their academic performance, the companies employed models of classification with mining concepts. Additionally, the quality of result classification can be substantially improved by using learning analytics and Educational Data Mining (EDM). The educational establishments are making an effort to lower the low student performance. To address this issue, numerous methods for assessing student performance have been devised, allowing the relevant faculties to intervene and enhance the final product. Three classes—Low Performance Student, Average Student, and Smart Student—were created using the K-Mean Clustering methodology for classifying student records. Features including grade point, number of deficits, student attendance, medium of education, and board of education are taken into account when classifying the data. In this case, the WEKA tool is also utilized for implementing the model and outcome assessments.

关键词： Deep learning Decision support systems Analytical models Data analysis Automation Education Companies

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Hybrid multi-criteria decision-making method with a bipolar fuzzy approach and its applications to economic condition analysis

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Engineering Applications of Artificial Intelligence 2024年 132卷

作者： Jana, Chiranjibe Simic, Vladimir Pal, Madhumangal Sarkar, Biswajit Pamucar, Dragan Tamil Nadu Chennai602105 India Faculty of Transport and Traffic Engineering University of Belgrade VojvodeStepe 305 Belgrade11010 Serbia Yuan Ze University College of Engineering Department of Industrial Engineering and Management Taoyuan City320315 Taiwan Department of Applied Mathematics with Oceanology and Computer Programming Vidyasagar University Midnapore721102 India Department of Industrial Engineering Yonsei University 50 Yonsei-ro Sinchon-dong Seodaemun-gu Seoul03722 Korea Republic of Center for Global Health Research Saveetha Medical College Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu 600077 India Department of Industrial Engineering and Management National Taipei University of Technology Taipei City 106344 Taiwan Department of Operations Research and Statistics Faculty of Organizational Sciences University of Belgrade Belgrade Serbia Department of Computer Science and Mathematics Lebanese American University Byblos 1102 2801 Lebanon

The bipolar fuzzy set and interval-valued bipolar fuzzy set efficiently analyse real-world problems where for each input of an object, there has counter information. This study's main objective is to lay a foundation for considering a new approach to the bipolar fuzzy hybrid operators tool applied in a realistic frame that contains uncertainties. In this view, some mixed aggregation operators, namely, the hybrid bipolar fuzzy weighted averaging geometric (H-BFWAG), hybrid bipolar fuzzy order weighted averaging geometric (H-BFOWAG), hybrid interval-valued bipolar fuzzy weighted averaging geometric (H-IVBFWAG), and hybrid interval-valued bipolar fuzzy order weighted averaging geometric (H-IVBFOWAG) operators are generated in bipolar fuzzy environment. A decision making approach is developed to build the multi-criteria decision making model. A numerical example is considered to study the different effects on various Indian economic sectors in the COVID-19 environment. Managerial implications are highlighted in between many industrial sectors on which the Indian economy depends and the country's gross domestic product depends on their inter-relationship. This study investigates COVID-19's impact on society and the economy in the Indian context. A comparative investigation is introduced with some pre-existing operators and the TOPSIS (technique for order of preference by similarity to ideal solution) approach to justify the proposed method. © 2024 Elsevier Ltd

关键词： COVID-19

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Slippery Lubricant-Infused Silica Nanoparticulate Film Processing for Anti-Biofouling Applications 73

Slippery Lubricant-Infused Silica Nanoparticulate Film Proce...

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73rd International Astronautical Congress, IAC 2022

作者： Sip, Yuen Yee Li Jacobs, Annabel Morales, Alejandra Sun, Mengdi Roberson, Luke B. Hummerick, Mary E. Roy, Herve Kik, Pieter Zhai, Lei Department of Materials Science & Engineering University of Central Florida FL United States Herbert Wertheim College of Engineering University of Florida FL United States Engineering Computer Programming and Technology Division Valencia College FL United States College of Optics and Photonics University of Central Florida FL United States Kennedy Space Center National Aeronautics and Space Administration FL United States Kennedy Space Center Amentum Services FL United States Burnett School of Biomedical Sciences University of Central Florida FL United States Department of Chemistry University of Central Florida FL United States

Microbial biofilm build-up in water distribution systems can pose a risk to human health and pipe material integrity. The impact is more devastating in space stations and to astronauts due to the isolation from necessary replacement parts and medical resources. As a result, there is a need for coatings to be implemented onto the inner region of the pipe to minimize the adherence and growth of biofilms. Lubricant-infused surfaces has been one such interesting material for anti-biofouling applications in which their slippery property promotes repellence to many liquids and thus prevents bacterial adherence. Textured and porous films are suitable substrate candidates to infuse and contain the lubricant. However, there is little investigation in utilizing a nanoporous thin film as the substrate material for lubricant infusion. A nanoporous film has high porosity within the structure which can promote greater lubricant infusion and retention. The implementation as a thin film structure aids to reduce material consumption and cost. In our study, we utilized a well-studied nanoporous thin film fabricated via layer-by-layer assembly of polycations and colloid silica and calcination for greater stability. The film was further functionalized to promote fluorinated groups upon the surface and pores of the film, which then could interact with a fluorinated lubricant. The pristine nanoporous film was characterized to determine its morphology, thickness, wettability, and porosity. The lubricant-infused film was then tested for its lubricant layer stability upon various washing conditions and its performance against bacterial biofilm adherence as a result of its slippery and thus anti-biofouling property. Copyright © 2022 by the International Astronautical Federation (IAF). All rights reserved.

关键词： Silica

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A Novel Solution to Enhance the Oxidative and Physical Properties of Cookies Using Maltodextrin-Based Nano-Sized Oils as a Fat Substitute

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ACS Omega 2025年第22期10卷 23111-23120页

作者： Raciye Meral Mehmet Mustafa Eki̇N Zafer Ceylan Aslıhan Alav Erol Kına Department of Food Engineering Faculty of Engineering Van Yüzüncü Yıl University Van 65080 Turkiye Özalp Vocational School Food Technology Program Van Yuzuncu Yıl University Van 65080 Turkiye Faculty of Science Department of Molecular Biology and Genetics Kutlubey Campus Bartin University Kutlubeyyazıcılar Village Bartin 74110 Turkiye Van Yüzüncü Yıl University Institute of Science Van 65080 Turkiye Computer Programming Program Özalp Vocational School Van Yüzüncü Yıl University Van 65100 Turkiye；Innovan Entrepreneurship Centre Van 65080 Turkiye

This study investigated the effects of maltodextrin-based nanoemulsions as fat substitutes in cookies, focusing on the oxidative stability and physical properties. Full-fat cookies (control, C) and 50% fat-reduced cookies with nanoemulsions (FC) were produced. The addition of nanoemulsions increased the cookie diameter from 46.3 mm (control) to 56.1 mm and reduced the thickness, resulting in a desirable texture. Initial hardness values (30.3 and 45.8 N) were lower in nanoemulsion samples and remained reduced over a 90 day storage period. Black cumin oil-loaded nanoemulsions provided the lowest peroxide values (1.7, 2.7, and 2.4 mequiv O2/kg), maintaining oxidative stability during storage. Final free fatty acid (FFA) values ranged from 0.23% to 0.44% after storage. Thiobarbituric acid (TBA) values indicated slower lipid oxidation, with values ranging from 1.47 to 2.51 mg MDA/kg on day 0 and increasing to a maximum of 4.13 mg MDA/kg by day 90 in fat-reduced cookies. Among the tested formulations, nanoemulsions enriched with black cumin oil demonstrated the highest effectiveness, yielding enhanced oxidative stability and improved quality characteristics. This study presents an innovative strategy by utilizing maltodextrin-based nanoemulsions containing naturally antioxidant-rich oils as fat replacers, offering a clean-label alternative to improve the oxidative resilience and physical quality of cookies.

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A Horizontal Federated Learning Approach to IoT Malware Traffic Detection: An Empirical Evaluation with N-BaIoT Dataset

A Horizontal Federated Learning Approach to IoT Malware Traf...

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International Conference on Advanced Communication technology (ICACT)

作者： Phuc Hao Do Tran Duc Le Vladimir Vishnevsky Aleksandr Berezkin Ruslan Kirichek The Bonch-Bruevich Saint-Petersburg State University of Telecommunications Saint-Petersburg Russia Danang Architecture University Da Nang Viet Nam Department of Communication Networks and Data Transmission at Bonch-Bruevich Saint-Petersburg State University of Telecommunications Russia University of Science and Technology - The University of Danang Da Nang Viet Nam Information Technology Faculty The University of Danang - University of Science and Technology Danang Vietnam V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences Moscow Russia Institute of Control Sciences of Russian Academy of Sciences Russia Department of Programming Engineering and Computer Science Bonch Bruevich Saint Petersburg State University of Telecommunications

The increasing prevalence of botnet attacks in IoT networks has led to the development of deep learning techniques for their detection. However, conventional centralized deep learning models pose challenges in simultaneously ensuring user data privacy and detecting botnet attacks. To address this issue, this study evaluates the efficacy of Federated Learning (FL) in detecting IoT malware traffic while preserving user privacy. The study employs N-BaIoT, a dataset of real-world IoT network traffic infected by malware, and compares the effectiveness of FL models using Convolutional Neural Network, Long Short-Term Memory, and Gated Recurrent Unit models with a centralized approach. The results indicate that FL can achieve high performance in detecting abnormal traffic in IoT networks, with the CNN model yielding the best results among the three models evaluated. The study recommends the use of FL for IoT malware traffic detection due to its ability to preserve data privacy.

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Uvl: Feature Modelling with the Universal Variability Language

SSRN

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SSRN 2024年

作者： Benavides, David Sundermann, Chico Feichtinger, Kevin Galindo, José A. Rabiser, Rick Thüm, Thomas Department of Computer Languages and Systems Universidad de Sevilla Av. Reina Mercedes Seville41012 Spain Institute of Software Engineering and Programming Languages University of Ulm Albert-Einstein-Allee 11 Ulm89069 Germany CRC 1608 KASTEL – Dependability of Software-intensive Systems Karlsruhe Institute of Technology Am Fasanengarten 5 Karlsruhe76131 Germany Christian Doppler Laboratory VaSiCS LIT CPS Lab Johannes Kepler University Linz Altenberger Straße 69 Linz4040 Austria

Feature modelling is a cornerstone of software product line engineering, providing a means to represent software variability through features and their relationships. Since its inception in 1990, feature modelling has evolved through various extensions, and after three decades of development, there is a growing consensus on the need for a standardised feature modelling language. Despite multiple endeavours to standardise variability modelling and the creation of various textual languages, researchers and practitioners continue to use their own approaches, impeding effective model sharing. In 2018, a collaborative initiative was launched by a group of researchers to develop a novel textual language for representing feature models. This paper introduces the outcome of this effort: the Universal Variability Language (\UVL), which is designed to be human-readable and serves as a pivot language for diverse software engineering tools. The development of \UVL drew upon community feedback and leveraged established literature in the field of variability modelling. The language is structured into three levels -Boolean, Arithmetic, and Type- and allows for language extensions to introduce additional constructs enhancing its expressiveness. \UVL is integrated into various existing software tools, such as FeatureIDE and FLAMA, and is maintained by a consortium of institutions. © 2024, The Authors. All rights reserved.

关键词： Modeling languages

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