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8th International Doctoral Workshop on Mathematical and engineering Methods in computer Science, MEMICS 2012

作者： Mikuš, Michal Sýs, Marek Institute of Mathematics and Applied Informatics Faculty of Electrical Engineering and Information Technology Slovak University of Technology in Bratislava Ilkovičova 3 812 19 Bratislava Slovakia

ISBN: (纸本)9783642360442

In this paper we examine the first working implementation of a fully homomorphic scheme from *** and ***. We implemented the ciphertext-only attack from [2] using the NTL library and show that only dimensions up to 128 are feasible for common computational power. We propose also two improvements of this attack that enable us to use the fastest variant of LLL from NTL and compare the results. © 2013 Springer-Verlag.

关键词： Security of data

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Integrating Data Protection at Interface of RISC-V Processor Core

Integrating Data Protection at Interface of RISC-V Processor...

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Panhellenic Conference on Electronics and Telecommunications (PACET)

作者： Ján Mach Lukáš Kohútka Pavel Čičák Institute of Computer Engineering and Applied Informatics Slovak University of Technology Slovakia Institute of Informatics Information Systems and Software Engineering Slovak University of Technology Slovakia

Fulfilling increasing performance demands of space and automotive applications can be problematic as high dependability is required. Memory is one of the most radiationsensitive parts, so it is often protected with information redundancy. This paper describes an integration of error correction and detection techniques into the interface of RISC-V processor to protect the data stored in the memory. We also provide a hardware-software interface for reporting errors and software routines for correcting data in memory. Our solution has a negligible impact (-3.9%) on the overall performance of the core with a small area and power consumption overhead.

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Application of Parallel Signal Processing Using Reverse Blockchain technology in Conditions of Network Instability 4

Application of Parallel Signal Processing Using Reverse Bloc...

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4th International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2024

作者： Khilenko, Volodymyr Zinchenko, Andrii Stepanov, Oleksii Kotuliak, Ivan Ries, Michal Institute of Computer Engineering and Applied Informatics Slovak University of Technology in Bratislava Faculty of Informatics and Information Technologies Bratislava Slovakia Institute of Transport Systems and Technologies National Academy of Sciences of Ukraine Dnipro Ukraine National University of Life and Environmental Sciences of Ukraine Kyiv Department of Information Technologies Ukraine Slovak University of Technology in Bratislava Faculty of Informatics and Information Technologies Bratislava Slovakia

ISBN: (纸本)9798350391183

The theses is in line with works aimed at studying the possibility of using mathematical algorithms for parallel processing of information in reverse blockchain technology. The paper examines the use of parallel signal processing algorithms to improve the speed and quality of control systems for autonomous dynamic objects using reverse blockchain technology. The use of reverse blockchain technology is due to the possibility of loss of control signals and, at the same time, the inadmissibility of loss of control in the event of changes in the conditions and parameters of the operation of the obj ect. The purpose of the work is to analyze the implementation of solutions for parallel information processing for various models of industrially produced equipment and to develop solutions to increase the efficiency of reverse blockchain technology when using parallel data processing. © 2024 IEEE.

关键词： blockchain dynamic objects and technological processes improving the quality of control systems instability of communication channels loss of information and control signals reverse blockchain technology

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Enhanced approach on solving Online Knapsack Problem to improve Load Balancing

Enhanced approach on solving Online Knapsack Problem to impr...

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International Conference on Telecommunications and Signal Processing (TSP)

作者： Marek Galinski Juraj Volko Ivan Kotuliak Institute of Computer Engineering and Applied Informatics Slovak University of Technology Bratislava Slovakia

ISBN: (数字)9781728163765

ISBN: (纸本)9781728163772

Distributing data flows of given bandwidth between various disjoint network paths of given bandwidth capacity, is a non-trivial task when the flows are considered unsplittable, thus we cannot send its packet via different paths, but all packets of a flow must be transmitted within the same path. This problem can be described as a modification of the well-known Knapsack problem. In this paper, we solve the Knapsack problem applied to resource allocation. We try to match items to individual knapsack and to use the maximum capacity of all knapsacks. Packing of items is managed by the QFLA algorithm, which tries to pack the coming item to knapsack with the biggest remaining capacity and if it is impossible then try to move items between knapsacks to make a bigger capacity of some knapsack and to place the item there. It is necessary to work in the real-time so we decided to use the structure of Binary Search Trees. It is used to keep knapsack ordered with its remaining capacity.

关键词： Time complexity Bandwidth Real-time systems Binary search trees Monitoring Approximation algorithms

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Accelerating SDN control plane with GPGPU-based packet classification

Accelerating SDN control plane with GPGPU-based packet class...

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International Conference on Emerging eLearning Technologies and Applications (ICETA)

作者： Martin Baláž Pavol Helebrandt Slovak University of Technology Institute of Computer Engineering and Applied Informatics Bratislava Slovakia

ISBN: (数字)9781728149677

ISBN: (纸本)9781728149684

The increasing number of network traffic and packet processing in Software Defined Networks can be a challenge for the slower handling performance by control plane in software. Parallel processing of network traffic offers a solution to increase the performance of software-based control planes. Integrating large amounts of additional general-purpose CPU cores is relatively costly for achieving a substantial boost in packet classification by parallel processing. In contrast, simplified compute cores in the GPUs of today deliver better performance in execution of massively parallel tasks and offer better scalability. The motivation of this work is to utilize the performance of the GPU computational power for acceleration of control plane. The focus of this paper is packet classification in parallel processing on GPU by applying the principles of parallel programming into existing algorithms.

关键词： Graphics processing units Acceleration Classification algorithms Firewalls (computing) Telecommunication traffic Process control

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The Calibration of Single Beam Distance Sensors based on Machine Learning Methods

The Calibration of Single Beam Distance Sensors based on Mac...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Biernacki, Piotr Ziebinski, Adam Syu, Jia-Hao Lin, Jerry Chun-Wei Silesian University of Technology Institute of Theoretical and Applied Informatics Polish Academy of Sciences Institute of Informatics Gliwice Poland Silesian University of Technology Institute of Informatics Gliwice Poland National Taiwan University Department of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798350324457

Smart cities require the use of many different types of sensors to make the communication, and distance sensors are one of the most commonly used elements in transportation systems and related infrastructures. The introduction of increasingly advanced autonomous systems in many areas of smart cities requires high measurement precision of the sensors used. High precision is essential for proper operation, long-term use, and safety in machine-to-machine or machine-to-human interactions. This paper presents a comparison of the accuracy of distance measurements for two commercially available single-beam LiDARs and two ultrasonic sensors. The aim of the research was to develop a calibration method in order to improve the accuracy of distance sensors. Based on the collected distance measurements, the sensors were calibrated using selected machine learning algorithms. The results of the experiments show the effectiveness of the proposed calibration methods, which yield an average mean absolute error (MAE) of 1.76 E-05 meters (m) and a root mean square error (RMSE) of 1.33 E-04 m for the tested sensors. © 2023 IEEE.

关键词： machine learning sensors calibration single-beam distance sensors

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Improving DL-Learner on a Malware Detection Use Case 36

Improving DL-Learner on a Malware Detection Use Case

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36th International Workshop on Description Logics, DL 2023

作者： Bisták, Tomáš Švec, Peter Kluka, Ján Šimko, Alexander Balogh, Štefan Homola, Martin Department of Applied Informatics Faculty of Mathematics Physics and Informatics Comenius University Mlynská dolina Bratislava842 48 Slovakia Institute of Computer Science and Mathematics Faculty of Electrical Engineering and Information Technology Slovak University of Technology Ilkovicova 3 Bratislava812 19 Slovakia

Automation of malware characterization has become increasingly important for early malware detection over the past decades. Since it is crucial to be able to perform malware detection transparently, explainable machine-learning methods may be considered particularly suitable for this application. In our previous work, we thus employed algorithms for learning description-logics concept expressions to obtain structured characterizations of malicious software. The results of our initial experiments, however, lead us to several inconsistencies in the behaviour of the studied algorithms included in DL-Learner, a widely adopted framework for concept learning. Hence, in this work, we make a couple of corrections to DL-Learner, along with a few further enhancements, and repeat the experiments to empirically assess the effects of our changes. The outcomes show that the modified algorithms can mark an improvement in terms of the predictability of their behaviour as well as evaluation metrics. © 2023 CEUR-WS. All rights reserved.

关键词： Malware

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Novel Noise Filter Techniques and Dynamic Ensemble Selection for Classification

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Recent Advances in computer Science and Communications 2022年第1期15卷 48-59页

作者： Nguyen, Thi Ngoc A. Nhu, Quynh P. Solanki, Vijender K. School of Applied Mathematics and Informatics Hanoi University of Science and Technology Hanoi Viet Nam CMC Institute of Science and Technology 11 Duy Tan Cau Giay Hanoi Viet Nam Department of Computer Science and Engineering CMR Institute of Technology TS Hyderabad India

Background: Ensemble selection is one of the most researched topics for ensemble learning. Researchers have been attracted to selecting a subset of base classifiers that may perform more helpful than the whole ensemble system classifiers. Dynamic Ensemble Selection (DES) is one of the most effective techniques in classification problems. DES systems select the most appropriate classifiers from the candidate classifier pool. Ensemble models that balance diversity and accuracy in the training process improve performance than the whole classifiers. Objective: In this paper, novel techniques are proposed by combining Noise Filter (NF) and Dynamic Ensemble System (DES) to have better predictive accuracy. In other words, a noise filter and DES make the data cleaner and DES improves the performance of classification. Methods: The proposed NF-DES model, which was demonstrated on twelve datasets, especially has three credit scoring datasets and a performance measure accuracy. Results: The results show that our proposed model is better than other models. Conclusion: The novel noise filer and dynamic ensemble learning with the aim to improve the classification ability are presented. To improve the performance of classification, noise filter with dynamic ensemble learning makes the noise data toward the correct class. Then, novel dynamic ensemble learning chooses the appropriate subset classifiers in the pool of base classifiers. © 2022 Bentham Science Publishers.

关键词： Support vector machines

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Comparison of planar crane models 19

Comparison of planar crane models

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19th Conference on applied Mathematics, APLIMAT 2020

作者： Grosinger, Patrik Šolek, Peter Rybár, Jan Institute of Applied Mechanics and Mechatronics Faculty of Mechanical Engineering Slovak University of Technology in Bratislava Námestie slobody 17 Bratislava812 31 Slovakia Institute of Automation Measurement and Applied Informatics Slovak University of Technology in Bratislava Mýtna 36 Bratislava811 07 Slovakia

ISBN: (纸本)9788022749831

This article focuses on analyzing various controlled planar dynamic crane models and provides simulations of multiple used approaches. A number of publications are devoted to the control of a plane crane model. This simplifying circumstance is justified, and most bridge cranes allow only one direction to shift the payload. It is therefore acceptable to replace this spatial system with a planar one. And the simplest controlled linearized model without friction and damping, non-linear model without friction and damping, and both mentioned models considering rolling friction between trolley and its rails. Models with viscous damping will not be considered because this damping is small for cranes. This article provides a comparison of individual computational approaches as well as comparisons with real-world crane response. Finally, there is a suitable and simple method of linearized system control with respect to rolling friction, which consists of a combination of feedback linearization method and PPM method. © 19th Conference on applied Mathematics, APLIMAT 2020 Proceedings. All rights reserved.

关键词： Feedback linearization

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AI Based Prediction for Heart Disease: A Comparative Analysis and an Improved Machine Learning Approach 6

AI Based Prediction for Heart Disease: A Comparative Analysi...

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6th Asian Conference on Artificial Intelligence technology, ACAIT 2022

作者： Raval, Jay Verma, Jai Prakash Islam, Sardar N.M. Jain, Rachna Thakur, Narina Institute of Technology Nirma University Computer Science Engineering Department Ahmedabad India Victoria University Applied Informatics Research Melbourne Australia Bhagwan Parshuram Institute of Technology Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9781665453110

Heart disease problems are growing day by day in the world. Many factors are responsible for increasing the chance of heart attack and any other disease. Many countries have a low level of cardiovascular competence in predicting heart disease-related issues. Finding the best accurate machine learning classifiers for various diagnostic uses by data mining and machine learning techniques aids in predicting whether or not the heart disease-related issue will occur. To predict heart disease, a number of supervised machine-learning algorithms are used and their effectiveness are evaluated. With the exceptionof MLP and KNN, all applied algorithms had their estimated feature significance scores for each feature. This helps to find the main factors affecting heart disease and the accuracy of the model, which helps to get the best prediction. At the end of the research the support vector machine gives us 87.91 % highest testing accuracy compare with all applied machine learning algorithm. © 2022 IEEE.

关键词： Data mining

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