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31st International Conference on Computer Graphics and Vision, GraphiCon 2021

作者： Semenov, Vitaly Shutkin, Vasily Zolotov, Vladislav Ivannikov Institute for System Programming of the Russian Academy of Sciences Alexander Solzhenitsyn st. 25 Moscow109004 Russia Moscow Institute of Physics and Technology 9 Institutskiy per. Dolgoprudny141701 Russia National Research University Higher School of Economics 11 Pokrovsky Bulvar Moscow109028 Russia

Rendering of large scenes in external memory is one of the most important problems of computer graphics, which is used in such areas as CAD/CAM/CAE, geoinformatics, project management, scientific visualization, virtual and augmented reality, computer games and animation. Unlike static scenes, for which a number of effective approaches have been proposed, in particular, levels of detail (LOD), rendering of large dynamic scenes with a given level of the tolerance and trustworthiness remains a big challenge. This paper discusses and investigates the possibility of using the previously proposed method of hierarchical dynamic levels of detail (HDLOD) for conservative rendering of dynamic scenes with a deterministic nature of events in external memory. The carried out series of computational experiments prove the feasibility and effectiveness of the method for real industrial scenes when employed in combination with memory management techniques. © 2021 Copyright for this paper by its authors.

关键词： Computer games

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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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Calculation of Signal Information Delay in Intelligent Communication Networks 3

Calculation of Signal Information Delay in Intelligent Commu...

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3rd IEEE KhPI Week on Advanced technology, KhPI Week 2022

作者： Kuchuk, Nina Mozhaiev, Mykhailo Kalinin, Yevhen Davydov, Viacheslav Mozhaev, Oleksandr Kuchuk, Heorhii National Technical University 'Kharkiv Polytechnic Institute' Department of Computer Engineering and Programming Kharkiv Ukraine Computer Technology and Telecommunications Researches Sector National Scientific Center «hon. Prof Bokarius Forensic Science Institute» Kharkiv Ukraine National University of Life and Environmental Sciences of Ukraine Tractors Cars and Bioenergy Resources Department Kyiv Ukraine Kharkiv National University of Internal Affairs Department of Cyber Security and Data Technologies Kharkiv Ukraine

ISBN: (数字)9798350399202

ISBN: (纸本)9798350399202

An analysis of the signaling systems used in the Intelligent Communication Network has been carried out. The main probabilistic characteristics of signal information delay are determined. A method for calculating these characteristics for such error correction methods is proposed: basic error control method and preventive cyclical control method. The analysis of the delay of signal units from loading the signal network is carried out. Simple formulas are proposed for approximating the load applied to the signaling link. © 2022 IEEE.

关键词： Error correction

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Lower bounds and optimal algorithms for smooth and strongly convex decentralized optimization over time-varying networks 21

Lower bounds and optimal algorithms for smooth and strongly ...

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Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Dmitry Kovalev Elnur Gasanov Alexander Gasnikov Peter Richtárik King Abdullah University of Science and Technology Thuwal Saudi Arabia KAUST Moscow Institute of Physics and Technology Moscow Russia and Institute for System Programming of the Russian Academy of Sciences Research Center for Trusted

ISBN: (纸本)9781713845393

We consider the task of minimizing the sum of smooth and strongly convex functions stored in a decentralized manner across the nodes of a communication network whose links are allowed to change in time. We solve two fundamental problems for this task. First, we establish the first lower bounds on the number of decentralized communication rounds and the number of local computations required to find an ∈-accurate solution. Second, we design two optimal algorithms that attain these lower bounds: (i) a variant of the recently proposed algorithm ADOM (Kovalev et al., 2021) enhanced via a multi-consensus subroutine, which is optimal in the case when access to the dual gradients is assumed, and (ii) a novel algorithm, called ADOM+, which is optimal in the case when access to the primal gradients is assumed. We corroborate the theoretical efficiency of these algorithms by performing an experimental comparison with existing state-of-the-art methods.

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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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Modern Information Technologies in Scientific Research and Educational Activities

arXiv

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arXiv 2024年

作者： Malakhov, Kyrylo Kaverinskiy, Vladislav Ivanova, Liliia Romanyuk, Oleksandr Romaniuk, Oksana Voinova, Svitlana Kotlyk, Sergii Sokolova, Oksana V. M. Glushkov Institute of Cybernetics The National Academy of Sciences of Ukraine Kyiv Ukraine Department of Wear-Resistant and Corrosion-Resistant Powder Construction Materials Frantsevic Institute for Problems in Material Science The National Academy of Sciences of Ukraine Kyiv Ukraine Separated Structural Subdivision «Odesa Technical Professional College of Odesa National University of Technology» Odessa Ukraine Vinnytsia National Technical university Vinnytsia Ukraine Vinnytsia National Technical university Vinnytsia Ukraine Odesa National University of Technology Odessa Ukraine The Institute of Computer Engineering Automation Robotics and Programming Odesa National University of Technology Odessa Ukraine Odesa National University of Technology Odessa Ukraine

This article presents a comprehensive methodology for enhancing the capabilities of conversational AI systems, focusing on ChatGPT, through the integration of ontology-driven structured prompts and meta-learning techniques. The proposed approach, exemplified by the RDWE OntoChatGPT system, demonstrates significant potential for improving productivity and expanding knowledge bases in specific subject domains and languages. By formalizing methods for creating and configuring structured prompts and context ontologies, the system enables ChatGPT to provide specific information using selected contexts, thereby enhancing its performance in handling data across diverse linguistic and domain contexts. Through empirical testing, the study evaluates the system’s effectiveness in providing accurate responses while also highlighting areas for further refinement. The methodology’s adaptability to other conversational AI systems based on large language models underscores its potential for broader application in natural language processing and dialogue systems. Future enhancements aim to refine structured prompts and incorporate additional input data to further improve system performance and accuracy. Overall, this research contributes to advancing the capabilities of conversational AI systems, paving the way for more sophisticated and effective human-computer interactions in various domains. © 2024, CC BY-SA.

关键词： Ontology

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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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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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Optimum Production through variational principle with the time quadratic demand, fuzzy time period and fuzzy integrand

arXiv

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arXiv 2023年

作者： Roul, J.N. Maity, K. Kar, S. Maiti, M. Patha Bhavana Visva-Bharati West Bengal Santiniketan731235 India Department of Mathematics M G Mahavidyalaya Bhupatinagar W.B. 721425 India Department of Mathematics National Institute of Technology W.B. Durgapur713209 India Department of Applied Mathematics with Oceanology Computer Programming Vidyasagar University W.B. Midnapore711201 India

Here a real life optimal control problem under fuzzy time period using variational principle is formulated and Solved. The unit production cost is a function of production rate and also dependent on raw material cost, development cost due to durability and wear-tear cost. The holding cost is assumed to be non-linear, dependent on time. The profit function which consists of revenue, production cost and holding cost is formulated as a Fuzzy-Final Time and Fixed State System optimal control problem with fuzzy time period. Here production rate is unknown and considered as a control variable and stock level is taken as a state variable. It is formulated to optimize the production rate so that total profit is maximum. The non-linear optimization technique-Generalised Reduced Gradient Method (LINGO 11.0) is used. The optimum results are illustrated both numerically and *** Codes 49XX Copyright © 2023, The Authors. All rights reserved.

关键词： Profitability

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Data poisoning: issues, challenges, and needs

Data poisoning: issues, challenges, and needs

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7th IET Smart Cities Symposium (SCS 2023)

作者： Mohammad Aljanabi Alaa Hamza Omran Maad M Mijwil Mostafa Abotaleb El-Sayed M. El-kenawy Sahar Yousif Mohammed Abdelhameed Ibrahim Department of computer College of Education Aliarqai university Baghdad Iraq University of information technology and communications Baghdad Iraq Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt Computer Science Department Computer Science & Information Technology College Anbar University Anbar Iraq Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

Data poisoning attacks, where adversaries manipulate training data to degrade model performance, are an emerging threat as machine learning becomes widely deployed in sensitive applications. This paper provides a comprehensive overview of data poisoning including attack techniques, adversary incentives, impacts on security and reliability, detection methods, defenses, and key research gaps. We examine label flipping, instance injection, backdoors, and other attack categories that enable malicious outcomes ranging from IP theft to accidents in autonomous systems. Promising detection approaches include statistical tests, robust learning, and forensics. However, significant challenges remain in translating academic defenses like adversarial training and sanitization into practical tools ready for operational use. With safety and trustworthiness at stake, more research on benchmarking evaluations, adaptive attacks, fundamental tradeoffs, and real-world deployment of defenses is urgently needed. Understanding vulnerabilities and developing resilient machine learning pipelines will only grow in importance as data integrity is fundamental to developing safe artificial intelligence.

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