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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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Estimating Solar Power Plant Data Using Time Series Analysis Methods

Estimating Solar Power Plant Data Using Time Series Analysis...

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Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Ebru Idman Emrah Idman Osman Yildirim Computer Programming Istanbul Arel University Istanbul Turkey Electronic Engineering Istanbul Arel University Istanbul Turkey

ISBN: (数字)9781728193526

ISBN: (纸本)9781728193533

When meteorological data such as temperature, precipitation, weather events and economic data such as stock prices and exchange rates reach large levels, it may be necessary to analyze them with time series analysis methods. The aim of this research is to analyze the data of solar power plants with time series and make predictions for the future. To achieve this goal, solar panel data with historical depth will be collected, the collected data will be trained and predicted by various time series analysis methods and comparison will be made according to the prediction success among the related models. Methodology: With this study, using Python 3.6 and R 3.6.1, the time series estimation models were modeled with AR, ARMA, SARIMA, DES and TES, the difference between the real value and the predicted value of the data was found by the RMSE (Square Root of the Mean Square Error) method and it was seen which model has the best ability to estimate the dataset. In addition, with the trend and seasonality of the data, detailed information about the dataset was obtained with descriptive analysis and graphics. As a result, it was seen that using SARIMA or TES models in the datasets that show seasonal change in the light of the studies and estimations performed gives better results.

关键词： Predictive models Market research Time series analysis Data models Meteorology Estimation Prediction algorithms

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Analysing the impact of COVID-19 outbreak and economic policy uncertainty on stock markets in major affected economies

Analysing the impact of COVID-19 outbreak and economic polic...

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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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Faster R-CNN Deep Learning Model for Pedestrian Detection from Drone Images

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SN computer Science 2020年第2期1卷 1-9页

作者： Hung, Goon Li Sahimi, Mohamad Safwan Bin Samma, Hussein Almohamad, Tarik Adnan Lahasan, Badr Intelligent Biometric Group School of Electrical and Electronic Engineering Engineering Campus Universiti Sains Malaysia Penang Nibong Tebal 14300 Malaysia Department of Computer Programming Faculty of Education–Shabwa University of Aden Shabwa Yemen School of Information Science and Technology Xiamen University Malaysia Jalan Sunsuria Bandar Sunsuria Selangor Darul Ehsan Sepang 43900 Malaysia

Pedestrian detection from a drone-based images has many potential applications such as searching for missing persons, surveillance of illegal immigrants, and monitoring of critical infrastructure. However, it is considered as a very challenge computer vision problem due to the variations in camera point of view, distance from pedestrian, changes in illuminations and weather conditions, variation in the surrounding objects, as well as present of human-like objects. Recently, deep learning-based models are getting more attention, and they have proven a great success in many object detection problems such as the detection of faces, breast masses, and vehicles. As such, this work aims to develop a deep learning-based model that will be applied for the problem of pedestrian detection from a drone-based images. Particularly, faster region-based convolutional neural network (Faster R-CNN) will be used to search for the present of a pedestrian inside the captured drone-based images. To assess the performances, a total of 1500 images were collected by S30W drone and these images were captured at different places, with various views and weather conditions, and at daytime and night-time. Results show that Faster R-CNN was able to achieve a promising result with 98% precision, 99% recall, and 98% F1 measure. Further analysis has been conducted by comparing the outcomes of Faster R-CNN with YOLO deep model on UAV123 publicly available dataset. The reported results indicated that both detection models almost reported very similar results. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Deep learning Drone images Faster R-CNN Pedestrian detection

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Automatic Detection of Smishing Attacks by Machine Learning Methods 1

Automatic Detection of Smishing Attacks by Machine Learning ...

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1st International Informatics and Software engineering Conference, IISEC 2019

作者： Balim, Caner Gunal, Efnan Sora Afyon Kocatepe University Dept. of Computer Programming Sandikli Vocational School Afyonkarahisar Turkey Eskisehir Osmangazi University Dept. of Computer Engineering Faculty of Engineering and Architecture Eskisehir Turkey

ISBN: (纸本)9781728139920

Phishing is a type of cyber-attack aiming to steal people's confidential information using a bait. Smishing is a form of phishing that is carried out via SMS messages sent to mobile phones. Attackers intend to steal the private information of their victims through the content they send in their SMS messages. Therefore, the detection of smishing messages has a vital role in information security. In this work, a machine learning-based model is proposed to detect smishing messages. Experiments conducted on a relatively large dataset, which contains legitimate and smishing messages, have shown that the proposed model offers a promising detection performance. © 2019 IEEE.

关键词： Large dataset

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A Top-Down Three-Way Merge Algorithm for HTML/XML Documents 1

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Science and Information Conference, SAI 2020

作者： Bakaoukas, Anastasios G. Bakaoukas, Nikolaos G. Faculty of Arts Science and Technology University of Northampton Waterside Campus University Drive NorthamptonNN1 5PH United Kingdom School of Engineering and Sciences Computer Science and Programming Department Foundation College Mitropoleos Street and Mnisikleous Campus Athens105 56 Greece

ISBN: (数字)9783030522490

ISBN: (纸本)9783030522483

Collaborative work, with the need to keep HTML/XML code up-to-date, is now becoming vital particularly in the Web Development field. In order to fully support collaborative work and resolve related problems the need has arisen for an optimum solution to the automated editing of a number of parallel copies originating from a single original HTML/XML code document with the additional requirement to subsequently merge the copies into a single updated document. A number of algorithms have been used in the past for the purpose, such as: Diff3, XmlDiff, DeltaXML & 3DM, but HTML/XML code complexity related issues have now called for an algorithm that is more specifically designed for the purpose. In this paper a new algorithmic approach to merging HTML/XML code documents is presented that is based on the "Three-way Merge" approach and the "Node-per-Node" comparison between ordered trees. For the creation of the actual merging function operating at the heart of the algorithm, a particular methodology was followed in which only the two "Current Versions" are required for the generation of the updated document, and no involvement of the "Original Document" is necessary. This is an important improvement over the currently existing algorithms because eliminates the well-known "Idempotent" problem in merging two HTML/XML documents. In addition, the algorithmic approach presented here allows for the identification and treatment of all the conflicts that arise during a HTML/XML code merging in an ordered and clearly specified manner. © 2020, Springer Nature Switzerland AG.

关键词： HTML

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Correction: Study of the hardfacing process using self-shielding flux-cored wire with an exothermic addition with a combined oxidizer of the Al-(CuO/Fe2O3) system

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The International Journal of Advanced Manufacturing Technology 2024年第7期134卷 3985-3985页

作者： Trembach, Bohdan Silchenko, Yury Balenko, Oleksii Hlavchev, Dmytro Kulahin, Kostiantyn Heiko, Hennadii Bellorin-Herrera, Oleksandra Khabosha, Serhii Zakovorotnyi, Oleksandr Trembach, Illia Private Joint Stock Company “Novokramatorsky Mashinostroitelny Zavod” Kiev Ukraine Department of Computer Engineering and Programming National Technical University “Kharkiv Polytechnic Institute” Kharkiv Ukraine Scientific Research Department Ivan Kozhedub Kharkiv National Air Force University Kharkiv Ukraine

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Methods for Using Class Based N-gram Language Models in the Kaldi Toolkit 23rd

Methods for Using Class Based N-gram Language Models in the ...

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23rd International Conference on Speech and computer, SPECOM 2021

作者： Pakoci, Edvin Popović, Branislav AlfaNum Speech Technologies Bulevar vojvode Stepe 40 Novi Sad21000 Serbia Faculty of Technical Sciences Department of Power Electronic and Telecommunication Engineering University of Novi Sad Trg Dositeja Obradovića 6 Novi Sad21000 Serbia Department of Music Production and Sound Design Academy of Arts Alfa BK University Nemanjina 28 Belgrade11000 Serbia Computer Programming Agency Code85 Odžaci Železnička 51 Odžaci25250 Serbia

ISBN: (纸本)9783030878016

This paper explains in detail several methods for utilization of class based n-gram language models for automatic speech recognition, within the Kaldi speech recognition framework. It reexamines an existing implementation for word-level grammars, and then presents two methods of converting ARPA-format language models into a corresponding weighted finite state transducer which is then used for decoding in Kaldi in a usual way. One of the methods relies on implicit expansion of class n-grams within the language model during the conversion into the transducer, and the other one on explicit expansion of the ARPA file by changing class n-gram entries into an array of regular class instance n-grams, while the conversion procedure remains unchanged. Some limitations and optional restrictions for the procedures are discussed as well. The proposed methods are tested on a model trained on a Serbian judicial corpus which includes classes for all types of personal nouns (first names, last names, place names, street names and organization names). © 2021, Springer Nature Switzerland AG.

关键词： Speech recognition

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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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Structure and Functioning of Information Systems of Background Monitoring of Landscape Elements of Gorgany Nature Reserve 10

Structure and Functioning of Information Systems of Backgrou...

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10th International Conference on Advanced computer Information Technologies, ACIT 2020

作者： Nykolaychuk, Yaroslav Petrashchuk, Yaroslav Slobodian, Olena Pitukh, Ihor Grynchyshyn, Taras Nykolaychuk, Lyubov Hryha, Volodymyr Ternopil National Economic University Department of Specialized Computer Systems Ternopil Ukraine Gorgany Nature Reserve Nadvirna Ukraine Nadvirna College by National Transport University Department of Programming and Information Technologies Nadvirna Ukraine Ivano-Frankivsk National Technical University of Oil and Gas Department of Social Communication and Law Ivano-Frankivsk Ukraine Vasyl Stefanyk Precarpathian National University Department of Computer Engineering and Electronics Ivano-Frankivsk Ukraine

This paper discusses the structural organization of the background monitoring system (BMS) of the transboundary UNESCO World Heritage Site 'Beech forests of the Carpathians and ancient beech forests of Germany' using actual data and information from Gorgany Nature Reserve in Ivano-Frankivsk, Ukraine. The features of the structure and functions of background monitoring of the protected natural resources, forests and wildlife of Gorgany Nature Reserve is based on modern methods of collecting and processing of digital data, implementation of the principles of Internet of Things and cloud technologies, combined with GPS-systems, sensor networks with the use of optical and fiber-optic telecommunication channels. The information technology of the background monitoring system is based on figurative cluster models of monitoring objects according to theoretical principles of statistical, correlatory, spectral, cluster, logic-statistical and entropy analysis. © 2020 IEEE.

关键词： Sensor networks

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