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SSRN 2023年

作者： Erbey, Ali Barışçı, Necaattin Department of Computer Programming Distance Education Vocational School Usak University Usak64200 Turkey Department of Information Systems Informatics Institute Gazi University Ankara Turkey Department of Computer Engineering Faculty of Technology Gazi University Ankara06500 Turkey

Lip reading is a technique that aims to understand spoken words by analyzing people's lip movements. Deep learning algorithms are used as a powerful tool for detecting and recognizing lip movements more accurately. In this study, we present a lip-reading architecture for the Turkish language. The architecture is trained to recognize Turkish words using 3DCNN (3D Convolutional Neural Network) architecture and two-stream deep learning methods. The model was trained on video data representing lip movements and learned to classify words accurately. The Turkish lip reading system has been trained and tested on a large Turkish lip reading dataset. In the experiments, an accuracy of 87.12% was achieved with the proposed model, indicating successful results in the Turkish language. The proposed model demonstrates high accuracy rates in recognizing Turkish words, showcasing its effectiveness. This paper introduces a novel architecture, a lip-reading system that employs deep learning techniques, providing a robust tool for successful Turkish lipreading applications. © 2023, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Recursive Neyman Algorithm for Optimum Sample Allocation under Box Constraints on Sample Sizes in Strata

arXiv

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arXiv 2023年

作者： Wesolowski, Jacek Wieczorkowski, Robert Wójciak, Wojciech Programming Coordination of Statistical Surveys and Registers Department Statistics Poland Aleja Niepodleglości 208 Warsaw00-925 Poland Faculty of Mathematics and Information Science Warsaw University of Technology ul. Koszykowa 75 Warsaw00-662 Poland

The optimum sample allocation in stratified sampling is one of the basic issues of survey methodology. It is a procedure of dividing the overall sample size into strata sample sizes in such a way that for given sampling designs in strata the variance of the stratified π estimator of the population total (or mean) for a given study variable assumes its minimum. In this work, we consider the optimum allocation of a sample, under lower and upper bounds imposed jointly on sample sizes in strata. We are concerned with the variance function of some generic form that, in particular, covers the case of the simple random sampling without replacement in strata. The goal of this paper is twofold. First, we establish (using the Karush-Kuhn-Tucker conditions) a generic form of the optimal solution, the so-called optimality conditions. Second, based on the established optimality conditions, we derive an efficient recursive algorithm, named RNABOX, which solves the allocation problem under study. The RNABOX can be viewed as a generalization of the classical recursive Neyman allocation algorithm, a popular tool for optimum allocation when only upper bounds are imposed on sample strata-sizes. We implement RNABOX in R as a part of our package stratallo which is available from the Comprehensive R Archive Network (CRAN) repository. Copyright © 2023, The Authors. All rights reserved.

关键词： Sampling

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The prediction of students' academic performances with a classification model built using data mining techniques

The prediction of students' academic performances with a cla...

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6th Smart Cities Symposium (SCS 2022)

作者： H. Alkattan M. Abotaleb A. Ali Subhi O. A. Adelaja A. Kadi H. K. Ibrahim Al-Mahdawi Department of System Programming South Ural State University Chelyabinsk Russia University of Diyala Diyala Iraq Information Communication and Technology Department Lagos State University Lagos Nigeria Department of Food and Biotechnology South Ural State University Chelyabinsk Russia

In the purview of most educational sectors today, numerous reviews regarding data mining have been the primary focus, with goals of discovering vast knowledge patterns for students' data. This paper focuses on building model that predicted the performance of computer science students in some courses they were examined through classification technique. The model built for the purpose of classification via decision tree classifier which was adopted in WEKA and KNIME analytics platform. The workflow model basically connected nodes to generate the decision trees while J4.8 Decision Tree Classifier programmable outputs were generated for the courses to obtain the confusion matrix and other instances. Bar Charts were plotted to display the students' performance in the courses. The mean absolute errors, RMS error and relative absolute error all read 0% while the kappa value obtained from the analysis ranged between 0.991 – 1.00 which perfectly agrees for most statistical values.

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Algebraic models of application of computer systems and information technologies

Algebraic models of application of computer systems and info...

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IEEE International Scientific Conference on Informatics

作者： Volodymyr Ovsyak Oleksandr Ovsyak Dmytro Bui Julia Petruszka Dept. Application of Informatics Kielce University of Technology Kielce Poland Dept. Kino and Arts National University of Culture and Arts L'viv Ukraine Department of Theory and Technology of Programming Taras Shevchenko National University of Kyiv Kyiv Ukraine Dept. Automatic and Information Technology Ukrainian University of Printing L'viv Ukraine

The mathematical models of diagrams of using cases of computer systems and information technologies (for Microsoft Visual *** platform) are built in the forms of Glushkov's algorithmic algebra systems, Zeitlin-Pogorilyi's modified algorithmic algebra systems, modified algorithmic algebras and primitive program algebras (PPA), which is an example of programing algebra class of composite type. Property of monotonicity and continuity is established for the branching operation of PPA as the corollary of the representation of branching operation in terms of set-theoretic constructions of function restriction over set using the properties of monotonicity and distributivity of function restriction and whole image of set with respect to binary relation.

关键词： Algebra Unified modeling language Computational modeling Computers information technology Mathematical model Visualization

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Supporting commissioning of production plants by model-based testing and model learning

Supporting commissioning of production plants by model-based...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Jan Ladiges Alexander Fay Christopher Haubeck Winfried Lamersdorf Sascha Lity Ina Schaefer Automation Technology Institute Helmut-Schmidt-University Hamburg Germany Distributed Systems and Information Systems University of Hamburg Hamburg Germany Institute for Programming and Reactive Systems Technische Universität Braunschweig Braunschweig Germany Technische Universitat Braunschweig Braunschweig Niedersachsen DE

During the commissioning phase of production systems the identification and correction of malfunctions is a tedious task mainly done manually by commissioning engineers. This task is of high importance because missed malfunctions may result in hazardous behavior during operation phase. At this point, regardless of the engineers expertise a systematic support can drastically decrease the risk of missed malfunctions. A promising systematic approach is to use engineering artifacts of the system design phase as an information source to identify unexpected behavior regarding the specification. This paper proposes such a systematic approach based on model-based testing resulting in automatic test case generation and execution which allows to support engineers with learned models representing the expected transient system behavior. Subsequently, the obtained models are used for detection of unexpected behavior during commissioning. The unexpected behavior is presented to a commissioning engineer who decides if the behavior (1) is correct and will be added to the models or (2) represents an identified system malfunction. The approach is evaluated on a demonstration plant.

关键词： Software Testing Sensors Actuators Production Hardware Monitoring

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Preface

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Annual Review in Automatic programming 1961年第C期2卷 vii-viii页

作者： Goodman, Richard Automatic Programming Information Centre Brighton College of Technology England United Kingdom

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Analysing the Impact of COVID-19 outbreak and Economic Policy Uncertainty on Stock Markets in Major Affected Economies 6

Analysing the Impact of COVID-19 outbreak and Economic Polic...

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6th Smart Cities Symposium, SCS 2022

作者： Matuka, Adelajda Asafo, Shuffield Seyram Eweke, Gamaliel O. Mishra, Pradeep Ray, Soumik Abotaleb, Mostafa Makarovskikh, Tatiana Alhussan, Amel Ali Abdelhamid, Abdelaziz A. El-Kenawy, El-Sayed M. Dutta, P.K. Chowdhury, Subrata University of Macerata Department of Economics and Law Italy Niger Delta University Department of Banking & Finance Nigeria J.N.K.V.V. College of Agriculture M.P. Powarkheda India Centurion University of Technology and Management Odisha Paralakhemundi India Department of System Programming South Ural State University Chelyabinsk454080 Russia Department of Computer Sciences College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Saudi Arabia Department of Computer Science Faculty of Computer and Information Sciences Ain Shams University Egypt Department of Communications and Electronics Delta Higher Institute of Engineering and Technolog Egypt School of Engineering and Technology Amity University West Bengal Kolkata India AP Chittoor India

ISBN: (纸本)9781839538544

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. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Investments

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A Horizontal Federated Learning Approach to IoT Malware Traffic Detection: An Empirical Evaluation with N-BaIoT Dataset 26

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

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26th International Conference on Advanced Communications technology, ICACT 2024

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

ISBN: (纸本)9791188428120

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. © 2024 Global IT Research Institute - GIRI.

关键词： Malware

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Min-max optimization over slowly time-varying graphs

arXiv

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arXiv 2023年

作者： Nguyen, Nhat Trung Rogozin, Alexander Metelev, Dmitriy Gasnikov, Alexander Moscow Institute of Physics and Technology Moscow Russia Institute for Information Transportation Problems Moscow Russia Caucasus Mathematic Center Adygh State University Moscow Russia Ivannikov Institute for System Programming The Russian Academy of Sciences Research Center for Trusted Artificial Intelligence Moscow Russia

Distributed optimization is an important direction of research in modern optimization theory. Its applications include large scale machine learning, distributed signal processing and many others. The paper studies decentralized min-max optimization for saddle point problems. Saddle point problems arise in training adversarial networks and in robust machine learning. The focus of the work is optimization over (slowly) time-varying networks. The topology of the network changes from time to time, and the velocity of changes is limited. We show that, analogically to decentralized optimization, it is sufficient to change only two edges per iteration in order to slow down convergence to the arbitrary time-varying case. At the same time, we investigate several classes of time-varying graphs for which the communication complexity can be reduced. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Methodology of Teaching programming Science Through Online Platforms

Methodology of Teaching Programming Science Through Online P...

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Informatics, Electronics, and Radio Engineering (PIERE), International Conference Problems of

作者： Akram Nishanov Oybek Allamov Janar Yusupova Marks Matyakubov Baxtiyor Qalandarov Reyimberganov Bahrom Department of Systematic and Practical Programming Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan Department of Software Engineering Urgench Branch of Tashkent University of Information Technology named after Muhammad al-Khwarizmi Urgench Uzbekistan Master of Urgench branch of TATU named after Muhammad al-Khwarizmi Urgench Branch of Tashkent University of Information Technology named after Muhammad al-Khwarizmi Urgench Uzbekistan Student of Urgench branch of TATU named after Muhammad al-Khwarizmi Urgench Branch of Tashkent University of Information Technology named after Muhammad al-Khwarizmi Urgench Uzbekistan

ISBN: (数字)9798331516321

ISBN: (纸本)9798331516338

This article presents an experiment conducted among students of the Faculty of “Software Engineering” of the Urgench branch of TATU named after Muhammad al-Khwarizmi. Teaching programming using online platforms in education has shown promising results in improving student outcomes. This article discusses the methodology used, the comparative results, and the impact of the platform on software education. The rapid adoption of online platforms in education has changed the way programming is taught, providing students with an interactive, hands-on learning environment. This shift is especially relevant in teaching programming, where real-time feedback and practice are essential to mastering algorithms and coding concepts. The study compared two groups: one was trained using the https://***/platform and the assignments were checked using this system, and the second group was trained using traditional methods. The results showed that students who used the online platform had a high level of mastery, which highlighted the effectiveness of integrating https://***/into programming education.

关键词： Education Software algorithms Real-time systems Software Encoding Computer science education Problem-solving Informatics programming profession

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