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International Conference on Process Control (PC)

作者： Lukáš Bartalský Cyril Belavý Michal Bartko Gabriel Hulkó Milan Kubiš Institute of Automation Measurement and Applied Informatics Faculty of Mechanical Engineering Slovak University of Technology in Bratislava Measurement and Applied Informatics Slovak University of Technology in Bratislava

ISBN: (纸本)9781538640128

In the paper a problem of casting die preheating control is solved. Concept of the control is designed based on distributed parameters systems. The design of the control structures are created within simulation studies, afterwards are connected into co-simulation regime network to tune the control parameters and then applied to control of preheating using PLC.

关键词： Casting Temperature Steady-state Heating systems Mathematical model Temperature measurement

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Best Selection of Generative Adversarial Networks Hyper-Parameters Using Genetic Algorithm

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SN computer Science 2021年第4期2卷 283页

作者： Alarsan, Fajr Ibrahem Younes, Mamoon Informatics and Decision Supporting Systems Higher Institute for Applied Sciences and Technology Damascus Syrian Arab Republic Faculty of Computer and Automation Engineering Damascus University Damascus Syrian Arab Republic

Generative adversarial networks (GANs) are most popular generative frameworks that have achieved compelling performance. They follow an adversarial approach where two deep models generator and discriminator compete with each other. In this paper, we propose a Generative Adversarial Network with best hyper-parameters selection to generate fake images for digit numbers 1–9 with generator and train discriminator to decide whereas the generated images are fake or true. Genetic algorithm (GA) technique was used to adapt GAN hyper-parameters, the resulted algorithm is named GANGA: generative adversarial network with genetic algorithm. The resulted algorithm has achieved high performance;it was able to get zero value of loss function for the generator and discriminator separately. Anaconda environment with tensorflow library facilitates was used;python as programming language was adapted with needed libraries. The implementation was done using MNIST dataset to validate the work. The proposed method is to let genetic algorithm choose best values of hyper-parameters depending on minimizing a cost function such as a loss function or maximizing accuracy function used to find best values of learning rate, batch normalization, number of neurons and a parameter of dropout layer. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Discriminator Generative adversarial networks (GAN) Generator Genetic algorithm Image synthesis MNIST dataset

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Composite metamorphic relations for integration testing 22

Composite metamorphic relations for integration testing

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8th International Conference on computer technology Applications, ICCTA 2022

作者： Iakusheva, Sofia Khritankov, Anton Phystech School of Applied Mathematics and Informatics Moscow Institute of Physics and Technology Russia Faculty of Computer Science Dept. of Software Engineering Hse University Russia

ISBN: (纸本)9781450396226

Metamorphic testing is a testing method for problems without test oracles. Integration testing allows for detecting errors in complex systems that may not be found during the testing of their components. In this paper, we propose a novel approach that applies metamorphic testing in integration testing. The main idea is to develop a composite metamorphic relation for the system represented as an acyclic graph. This relation is a logical function of metamorphic relations for the parts of the system (vertices of the graph). It takes into account the features of the parts. Also, it can simplify the search for failure by identifying the subsystem with error. In this paper's theoretical part, we describe an algorithm of relation design. Then, we apply our method to a bioinformatics system for comparative genetic analysis of tissues using production tools. This experiment proves our method can be applied to real-life pipelines and find errors in them. © 2022 ACM.

关键词： Integration testing

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AIANTAS) for IoT Cyberspace - An Upcoming Cloud-based Security Architecture for Police Authorities 9

AIANTAS) for IoT Cyberspace - An Upcoming Cloud-based Securi...

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9th IEEE International Conference on Information, Communication and Networks, ICICN 2021

作者： Memos, Vasileios A. Psannis, Konstantinos E. Ishibashi, Yutaka University of Macedonia School of Information Sciences Dept of Applied Informatics Thessaloniki Greece Nagoya Institute of Technology School of Engineering Dept of Computer Science Nagoya Japan

ISBN: (纸本)9780738113456

In this research work, we investigate the effects of cybercrime and focus on concerns about cybercrime expansion. Since black-hat hackers are constantly one step ahead of white-hat hackers and exploit new sophisticated hack-tools to intrude to devices and networks to commit cybercrime for different reasons, the utilization of the available emerging technologies can provide better privacy and protection level, while they can be used to combat cybercriminal attacks. This shift to new technologies and methods is regarded to be an urgent necessity today. Therefore, we demonstrate a potential novel security mechanism for Police Authorities, named 'Artificial Intelligence ANTi-Attack System' or simply 'AIANTAS' that aspires to improve the detection rate of cybercriminals and lead to their faster arrest. This model can be integrated into the other National Security defense mechanisms both in Greece and worldwide. © 2021 IEEE.

关键词： Data Analytics

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Evolution of web applications with aspect-oriented design patterns

Evolution of web applications with aspect-oriented design pa...

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2nd International Workshop on Adaptation and Evolution in Web Systems engineering, AEWSE 2007

作者： Bebjak, Michal Vranić, Valentino Dolog, Peter Institute of Informatics and Software Engineering Faculty of Informatics and Information Technology Slovak University of Technology Ilkovičova 3 84216 Bratislava 4 Slovakia Department of Computer Science Aalborg University Fredrik Bajers Vej 7 DK-9220 Aalborg EAST Denmark

It is more convenient to talk about changes in a domainspecific way than to formulate them at the programming construct level or-even worse-purely lexical level. Using aspect-oriented programming, changes can be modularized and made reapplicable. In this paper, selected change types in web applications are analyzed. They are expressed in terms of general change types which, in turn, are implemented using aspect-oriented programming. Some of general change types match aspect-oriented design patterns or their combinations.

关键词： Aspect oriented programming

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The sensitivity of language models and humans to Winograd schema perturbations 58

The sensitivity of language models and humans to Winograd sc...

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58th Annual Meeting of the Association for Computational Linguistics, ACL 2020

作者： Abdou, Mostafa Ravishankar, Vinit Barrett, Maria Belinkov, Yonatan Elliott, Desmond Søgaard, Anders Department of Computer Science University of Copenhagen Department of Informatics University of Oslo John A. Paulson School of Engineering and Applied Sciences Harvard University Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology

ISBN: (纸本)9781952148255

Large-scale pretrained language models are the major driving force behind recent improvements in performance on the Winograd Schema Challenge, a widely employed test of commonsense reasoning ability. We show, however, with a new diagnostic dataset, that these models are sensitive to linguistic perturbations of the Winograd examples that minimally affect human understanding. Our results highlight interesting differences between humans and language models: language models are more sensitive to number or gender alternations and synonym replacements than humans, and humans are more stable and consistent in their predictions, maintain a much higher absolute performance, and perform better on non-associative instances than associative ones. Overall, humans are correct more often than out-of-the-box models, and the models are sometimes right for the wrong reasons. Finally, we show that fine-tuning on a large, task-specific dataset can offer a solution to these issues. © 2020 Association for Computational Linguistics

关键词： Computational linguistics

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On using of random access machine simulators in teaching of theoretical computer science 09

On using of random access machine simulators in teaching of ...

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International Conference on computer Systems and Technologies and Workshop for PhD Students in Computing, CompSysTech'09

作者： Chudá, Daniela Nehéz, Martin Čerňanský, Michal Institute of Informatics and Software Engineering Faculty of Informatics and Information Technology Slovak University of Technology Ilkovičova 3 842 16 Bratislava 5 Slovakia Department of Information Technologies VSM School of Management City University of Seattle Bezručova 64 911 01 Trenčín Slovakia Panónska cesta 17 Bratislava 851 04 Slovakia Institute of Applied Informatics Faculty of Informatics and Information Technology Slovak University of Technology Ilkovičova 3 842 16 Bratislava 5 Slovakia

ISBN: (纸本)9781605589862

An objective of this paper is to present experiences with teaching of an abstract computational model Random Access Machine (RAM) as a part of the theoretical computer science course. A possibility of using simulation tools of RAM in such a teaching process are discussed as well. Key features and requirements of well designed RAM simulators are indicated. Our experiences with teaching process with RAM simulators at our university are also reported in this paper. © 2009 ACM.

关键词： Simulators

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Implementing a Robust and Scalable Detect-and-Avoid Algorithm for UAV Navigation in ROS 15

Implementing a Robust and Scalable Detect-and-Avoid Algorith...

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15th International Conference on Information, Intelligence, Systems and Applications, IISA 2024

作者： Meimetis, Dimitrios Daramouskas, Ioannis Lappas, Vaios Perikos, Isidoros Kapoulas, Vaggelis Paraskevas, Michael Nkua Department of Aerospace Science & Technology Athens Greece Computer Technology Institute and Press 'Diophantus' Patras Greece UoP Applied Mechanics Lab Patras Greece UoP Computer Engineering and Informatics Department Patras Greece Computer Technology Institute and Press 'Diophantus' University of Peloponnese Electrical and Computer Engineering Dept. Patras Greece

ISBN: (纸本)9798350368833

Unmanned aerial vehicles (UAVs) have garnered significant attention from the research community during the last decade, due to their diverse capabilities and potential applications. One of the most critical functions that drones must execute efficiently is navigation in real-world environments. This paper presents a decentralized approach for enabling unmanned aerial vehicles (UAVs) to navigate safely in unknown environments and avoid obstacles. Leveraging the Optimal Reciprocal Collision Avoidance (ORCA) algorithm, implemented in the Robot Operating System (ROS), our method facilitates conflict detection and resolution in 2D environments. Through simulations using ROS, Gazebo, and Iris drones, we validate the effectiveness of our approach in scenarios with initial trajectory conflicts. Our work addresses the pressing need for UAVs to autonomously plan and execute safe flights, laying the groundwork for enhanced UAV capabilities in various real-world applications. The simulation results demonstrate the efficiency and robustness of our approach. © 2024 IEEE.

关键词： Drones

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RerrFact: Reduced Evidence Retrieval Representations for Scientific Claim Verification

RerrFact: Reduced Evidence Retrieval Representations for Sci...

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2022 Workshop on Scientific Document Understanding, SDU 2022

作者： Rana, Ashish Khanna, Deepanshu Ghosal, Tirthankar Singh, Muskaan Singh, Harpreet Rana, Prashant Singh School of Business Informatics and Mathematics University of Mannheim Germany Department of Electronics & Communication Engineering Thapar Institute of Engineering & Technology India Institute of Formal and Applied Linguistics Faculty of Mathematics and Physics Charles University Czech Republic Department of Computer Science & Engineering Thapar Institute of Engineering & Technology India

Exponential growth in digital information outlets and the race to publish has made scientific misinformation more prevalent than ever. However, the task to fact-verify a given scientific claim is not straightforward even for researchers. Scientific claim verification requires in-depth knowledge and great labor from domain experts to substantiate supporting and refuting evidence from credible scientific sources. The SciFact dataset and corresponding task provide a benchmarking leaderboard to the community to develop automatic scientific claim verification systems via extracting and assimilating relevant evidence rationales from source abstracts. In this work, we propose a modular approach that sequentially carries out binary classification for every prediction subtask as in the SciFact leaderboard. Our simple classifier-based approach uses reduced abstract representations to retrieve relevant abstracts. These are further used to train the relevant rationale-selection model. Finally, we carry out two-step stance predictions that first differentiate non-relevant rationales and then identify supporting or refuting rationales for a given claim. Experimentally, our system RerrFact with no fine-tuning, simple design, and a fraction of model parameters fairs competitively on the leaderboard against large-scale, modular, and joint modeling approaches. © 2022 Copyright for this paper by its authors.

关键词： Abstracting

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Big Data, Machine Learning, and Applications 1

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丛书名： Lecture Notes in Electrical engineering

1000年

作者： Malaya Dutta Borah Dolendro Singh Laiphrakpam Nitin Auluck Valentina Emilia Balas

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