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Waypoint routing on bounded treewidth graphs

Autoři
Schierreich, Š.; Suchý, O.
Rok
2022
Publikováno
Information Processing Letters. 2022, 173 ISSN 0020-0190.
Typ
Článek
Anotace
In the Waypoint Routing Problem one is given an undirected capacitated and weighted graph G, a source-destination pair s, t∈V(G) and a set W⊆V(G), of waypoints. The task is to find a walk which starts at the source vertex s, visits, in any order, all waypoints, ends at the destination vertex t, respects edge capacities, that is, traverses each edge at most as many times as is its capacity, and minimizes the cost computed as the sum of costs of traversed edges with multiplicities. We study the problem for graphs of bounded treewidth and present a new algorithm for the problem working in 2^{O(tw)}·n time, significantly improving upon the previously known algorithms. We also show that this running time is optimal for the problem under Exponential Time Hypothesis

A Design and Stand Tests of Real-time Vehicle Active Suspension

Autoři
Rok
2021
Publikováno
international scientific journal tran&MOTAUTO WORLD. 2021, 2021(4), 116-119. ISSN 2367-8399.
Typ
Článek
Anotace
The paper deals with innovations in vehicle suspension technology developed in Josef Bozek´s Research Center of Combustion Engines and Automobiles at CTU in Prague, Czech Republic. A unique innovative suspension system that uses a linear electric motor as a controlled actuator has been designed. Many experiments on energy management in the system have been accomplished. In order to verify various control strategies and to test different ways of energy consumption optimization we designed and constructed a unique one-quarter-car test stand. To realize simulation and practical experiments at the test stand it is necessary to find a proper experimental road disturbance signal to excite the active suspension system. The disturbance signal is applied on one more linear motor that is placed under a wheel of the one-quarter-car test stand to excite the active suspension system. The paper deals with the way and results of experimental verification of vehicle active suspension behavior when robust control is applied and also with the energy management strategy that is used in the system. A modified H-infinity controller that enables to set energy management strategy is mentioned in the paper. At the close of the paper, some experiments taken on the one quarter-car model and their evaluation are discussed.

A Design and Stand Tests of Real-timeVehicle Active Suspension

Autoři
Rok
2021
Publikováno
Proceedings of the VII International Scientific Congress "Innovations 21". Sofia: Scientific-technical union of mechanical engineering, 2021. p. 32-36. vol. 1. ISSN 2603-3763.
Typ
Stať ve sborníku
Anotace
In the paper, energy recuperation and management in automotive suspension systems with linear electric motors controlled using a proposed H∞ controller to obtain a variable mechanical force for a car damper is presented. Vehicle suspensions in which forces are generated in response to feedback signals by active elements obviously offer increased design flexibility compared to the conventional suspensions using passive elements such as springs and dampers. The main advantage of the proposed solution using a linear AC motor is the possibility to generate desired forces acting between the unsprung and sprung masses of the car, providing good insulation of the car sprung mass from the road surface disturbances. In addition, under certain circumstances using linear motors as actuators enables to transform mechanical energy of the vertical car vibrations to electrical energy, accumulate it, and use it when needed. Energy flow control (management) enables to reduce or even eliminate the demands concerning the external power source.

A characterization of Sturmian sequences by indistinguishable asymptotic pairs

Autoři
Barbieri, S.; Labbé, S.; Starosta, Š.
Rok
2021
Publikováno
European Journal of Combinatorics. 2021, 95 ISSN 0195-6698.
Typ
Článek
Anotace
We give a new characterization of biinfinite Sturmian sequences in terms of indistinguishable asymptotic pairs. Two asymptotic sequences on a full -shift are indistinguishable if the sets of occurrences of every pattern in each sequence coincide up to a finitely supported permutation. This characterization can be seen as an extension to biinfinite sequences of Pirillo’s theorem which characterizes Christoffel words. Furthermore, we provide a full characterization of indistinguishable asymptotic pairs on arbitrary alphabets using substitutions and biinfinite characteristic Sturmian sequences. The proof is based on the well-known notion of derived sequences.

Active Directory Kerberoasting Attack: Detection using Machine Learning Techniques

Autoři
Kotlaba, L.; Fornůsek, S.; Lórencz, R.
Rok
2021
Publikováno
Proceedings of the 7th International Conference on Information Systems Security and Privacy. Madeira: SciTePress, 2021. p. 376-383. ISSN 2184-4356. ISBN 978-989-758-491-6.
Typ
Stať ve sborníku
Anotace
Active Directory is a prevalent technology used for managing identities in modern enterprises. As a variety of attacks exist against Active Directory environment, its security monitoring is crucial. This paper focuses on detection of one particular attack - Kerberoasting. The purpose of this attack is to gain access to service accounts’ credentials without the need for elevated access rights. The attack is nowadays typically detected using traditional ”signature-based” detection approaches. Those, however, often result in a high number of false alerts. In this paper, we adopt machine learning techniques, particularly several anomaly detection al- gorithms, for detection of Kerberoasting. The algorithms are evaluated on data from a real Active Directory environment and compared to the traditional detection approach, with a focus on reducing the number of false alerts.

Aggregate Function Generalization to Temporal Data

Rok
2021
Publikováno
2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI). Los Alamitos: IEEE Computer Society, 2021.
Typ
Stať ve sborníku
Anotace
In this article, we define an approximate generalization of aggregate functions for relational data with temporal attributes. This generalization is parametrized to allow simulation of a range of common aggregate functions and optionally take into account time. The parameters are not optimized, but we rather rely on repeated stochastic sampling of the parameters. We then apply a common regularized linear model to train a model on this high-dimensional space. Experimental results on 11 datasets suggest that there are datasets where incorporating time dimension into the model leads to an improvement in the predictive accuracy of the trained models.

Application of Distance Metric Learning to Automated Malware Detection

Rok
2021
Publikováno
IEEE Access. 2021, 2021(9), 96151-96165. ISSN 2169-3536.
Typ
Článek
Anotace
Distance metric learning aims to find the most appropriate distance metric parameters to improve similarity-based models such as k -Nearest Neighbors or k -Means. In this paper, we apply distance metric learning to the problem of malware detection. We focus on two tasks: (1) to classify malware and benign files with a minimal error rate, (2) to detect as much malware as possible while maintaining a low false positive rate. We propose a malware detection system using Particle Swarm Optimization that finds the feature weights to optimize the similarity measure. We compare the performance of the approach with three state-of-the-art distance metric learning techniques. We find that metrics trained in this way lead to significant improvements in the k -Nearest Neighbors classification. We conducted and evaluated experiments with more than 150,000 Windows-based malware and benign samples. Features consisted of metadata contained in the headers of executable files in the portable executable file format. Our experimental results show that our malware detection system based on distance metric learning achieves a 1.09 % error rate at 0.74 % false positive rate (FPR) and outperforms all machine learning algorithms considered in the experiment. Considering the second task related to keeping minimal FPR, we achieved a 1.15 % error rate at only 0.13 % FPR.

Automatic Detection and Decryption of AES by Monitoring S-box Access

Autoři
Kokeš, J.; Matějka, J.; Lórencz, R.
Rok
2021
Publikováno
Proceedings of the 7th International Conference on Information Systems Security and Privacy. Madeira: SciTePress, 2021. p. 172-180. ISSN 2184-4356. ISBN 978-989-758-491-6.
Typ
Stať ve sborníku
Anotace
In this paper we propose an algorithm that can automatically detect the use of AES and automatically recover both the encryption key and the plaintext. It makes use of the fact that we can monitor accesses to the AES S-Box and deduce the desired data from these accesses; the approach is suitable to software-based AES implementations, both naíve and optimized. To demonstrate the feasibility of this approach we designed a tool which implements the algorithm for Microsoft Windows running on the Intel x86 architecture. The tool has been successfully tested against a set of applications using different cryptographic libraries and common user applications.

Automorphisms of the cube n^d

Autoři
Dvořák, P.; Valla, T.
Rok
2021
Publikováno
Discrete Mathematics. 2021, 2021(344(3)), ISSN 0012-365X.
Typ
Článek
Anotace
Consider a hypergraph n^d where the vertices are points of the d-dimensional cube [n]^d and the edges are all sets of n points such that they are in one line. We study the structure of the group of automorphisms of n^d, i.e., permutations of points of [n]^d preserving the edges. In this paper we provide a complete characterization. Moreover, we consider the Colored Cube Isomorphism problem of deciding whether for two colorings of the vertices of n^d there exists an automorphism of n^d preserving the colors. We show that this problem is GI-complete.

Backward Pattern Matching on Elastic Degenerate Strings

Autoři
Procházka, P.; Cvacho, O.; Krčál, L.; Holub, J.
Rok
2021
Publikováno
Proceedings of 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021). Lisboa: SCITEPRESS – Science and Technology Publications, Lda, 2021. p. 50-59. vol. 3. ISSN 2184-4305. ISBN 978-989-758-490-9.
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Stať ve sborníku
Anotace
Recently, the concept of Elastic Degenerate Strings (EDS) was introduced as a way of representing a sequenced population of the same species. Several on-line Elastic Degenerate String Matching (EDSM) algorithms were presented so far. Some of them provide practical implementation. We propose a new on-line EDSM algorithm BNDM-EDS. Our algorithm combines two traditional algorithms BNDM and the Shift-And that were adapted to the specifics needed by Elastic Degenerate Strings. BNDM-EDS is running in O (Nmd m w e) worst-case time. This implies O (Nm) time for small patterns, where m is the length of the searched pattern, N is the size of EDS, and w is the size of the computer word. The algorithm uses O (N + n) space, where n is the length of EDS. BNDM-EDS requires a simple preprocessing step with time and space O (m). Experimental results on real genomic data show superiority of BNDM-EDS over state-of-the-art algorithms.

Binary intersection formalized

Autoři
Holub, Š.; Starosta, Š.
Rok
2021
Publikováno
Theoretical Computer Science. 2021, 866 14-24. ISSN 0304-3975.
Typ
Článek
Anotace
We provide a reformulation and a formalization of the classical result by Juhani Karhumäki characterizing intersections of two languages of the form $\{x,y\}^* \cap \{u,v\}^*$. We use the terminology of morphisms which allows to formulate the result in a shorter and more transparent way, and we formalize the result in the proof assistant Isabelle/HOL.

Can investment incentives potentially cause unemployment? An empirical analysis of the relationship between FDI and employment based on the OLI framework

Autoři
Evan, T.; Bolotov, I.
Rok
2021
Publikováno
Central European Business Review. 2021, 11(03), 1-17. ISSN 1805-4862.
Typ
Článek
Anotace
Unemployment, particularly in depressed regions, is more often than not used by politicians as the main argument for investment incentives provided to MNCs. This paper applies Dunning’s OLI Framework to the relationship between FDI and employment with the assumption that political negotiation between MNCs and the host government might actually have zero effect or a negative effect on employment. Since the last letter of OLI, internalization, suggests that MNCs optimize all production factors available to them and “subsidies” provided to MNCs by governments decrease the relative price of capital, MNCs may try to use more labour-saving techniques. Two hypotheses are tested using the dynamic panel model (DPD) and Granger causality tests for 193 countries for the years 1985–2019 where the first is supported with no strong relationship discovered between the variables. The results of the paper should support debate on the efficiency of investment incentives.

Comparison of OpTeX with other formats: LaTeX and ConTeXt

Autoři
Rok
2021
Publikováno
TUGboat, The Communication of the TeX Users Group. 2021, 2021(42:1), ISSN 0896-3207.
Typ
Článek
Anotace
OpTEX [1] was introduced in an article [2] in the previous issue of TUGboat. It is a macro package that creates a format for LuaTEX. Its features are comparable with other formats like LaTEX or ConTEXt. One may ask why to use a new format, particularly when it requires a different markup syntax. I try to answer this question here. I present a comparison among the LaTEX, ConTEXt, and OpTEX formats, from various points of view.

Data Structures to Represent a Set of k-long DNA Sequences

Autoři
Chikhi, R.; Holub, J.; Medvedev, P.
Rok
2021
Publikováno
ACM Computing Surveys. 2021, 54(1), 17:1-17:22. ISSN 0360-0300.
Typ
Článek
Anotace
The analysis of biological sequencing data has been one of the biggest applications of string algorithms. The approaches used in many such applications are based on the analysis of k-mers, which are short fixed-length strings present in a dataset. While these approaches are rather diverse, storing and querying a k-mer set has emerged as a shared underlying component. A set of k-mers has unique features and applications that, over the past 10 years, have resulted in many specialized approaches for its representation. In this survey, we give a unified presentation and comparison of the data structures that have been proposed to store and query a k-mer set. We hope this survey will serve as a resource for researchers in the field as well as make the area more accessible to researchers outside the field.

Deep Variational Autoencoder with Shallow Parallel Path for Top-N Recommendation (VASP)

Autoři
Vančura, V.; Kordík, P.
Rok
2021
Publikováno
Artificial Neural Networks and Machine Learning – ICANN 2021. Cham: Springer, 2021. p. 138-149. V. vol. 12895. ISSN 0302-9743. ISBN 978-3-030-86383-8.
Typ
Stať ve sborníku
Anotace
The recently introduced Embarrasingelly Shallow Autoencoder (EASE) algorithm presents a simple and elegant way to solve the top-N recommendation task. In this paper, we introduce Neural EASE to further improve the performance of this algorithm by incorporating techniques for training modern neural networks. Also, there is a growing interest in the recsys community to utilize variational autoencoders (VAE) for this task. We introduce Focal Loss Variational AutoEncoder (FLVAE), benefiting from multiple non-linear layers without an information bottleneck while not overfitting towards the identity. We show how to learn FLVAE in parallel with Neural EASE and achieve state-of-the-art performance on the MovieLens 20M dataset and competitive results on the Netflix Prize dataset.

Detection of HTTPS Brute-Force Attacks with Packet-Level Feature Set

Autoři
Luxemburk, J.; Hynek, K.; Čejka, T.
Rok
2021
Publikováno
11th Annual Computing and Communication Workshop and Conference (CCWC2021). Piscataway (New Jersey): IEEE, 2021. p. 0115-0123. ISBN 978-0-7381-4394-1.
Typ
Stať ve sborníku
Anotace
This paper presents a novel approach to detect brute-force attacks against web services in high-speed networks. The prevalence of brute-force attacks is so high that service providers, such as ISPs or web-hosting providers, cannot depend on their customers' host-based defenses. Moreover, the rising usage of encryption makes it more difficult to detect attacks on the network level. In our research, we created a dataset, which consists of 1.8 million extended IP flows from a backbone network combined with IP flows generated with three popular open-source brute-forcing tools. We identified a distinctive packet-level feature set and trained a machine-learning classifier with a false positive rate of 10^-4 and a true positive rate (the ratio of discovered attacks) of 0.938. The achieved results surpass the state-of-the-art solutions and show that the developed HTTPS brute-force detection algorithm is viable for production deployment.

Dynamic Neural Diversification: Path to Computationally Sustainable Neural Networks

Rok
2021
Publikováno
Artificial Neural Networks and Machine Learning – ICANN 2021. Cham: Springer, 2021. p. 235-247. 1. vol. 12892. ISSN 1611-3349. ISBN 978-3-030-86340-1.
Typ
Stať ve sborníku
Anotace
Small neural networks with a constrained number of trainable parameters, can be suitable resource-efficient candidates for many simple tasks, where now excessively large models are used. However, such models face several problems during the learning process, mainly due to the redundancy of the individual neurons, which results in sub-optimal accuracy or the need for additional training steps. Here, we explore the diversity of the neurons within the hidden layer during the learning process, and analyze how the diversity of the neurons affects predictions of the model. As following, we introduce several techniques to dynamically reinforce diversity between neurons during the training. These decorrelation techniques improve learning at early stages and occasionally help to overcome local minima faster. Additionally, we describe novel weight initialization method to obtain decorrelated, yet stochastic weight initialization for a fast and efficient neural network training. Decorrelated weight initialization in our case shows about 40% relative increase in test accuracy during the first 5 epochs.

Economic Conditions for Innovation: Private vs. Public Sector

Autoři
Evan, T.; Holý, V.
Rok
2021
Publikováno
Socio-Economic Planning Sciences. 2021, 76 1-15. ISSN 0038-0121.
Typ
Článek
Anotace
The Hicks induced innovation hypothesis states that a price increase of a production factor is a spur to the invention. We propose an alternative hypothesis restating that a spur to the invention requires not only an increase of one factor but also a decrease of at least one other factor to offset the companies’ cost. We illustrate the need for our alternative hypothesis in a historical example of the industrial revolution in the United Kingdom. Furthermore, we econometrically evaluate both hypotheses in a case study of research and development (R&D) in 29 OECD countries from 2003 to 2017. Specifically, we investigate the dependence of investments to R&D on the economic environment represented by average wages and oil prices using panel regression. We find that our alternative hypothesis is supported for R&D funded and/or performed by business enterprises while the original Hicks hypothesis holds for R&D funded by the government and R&D performed by universities. Our results reflect that the business sector is significantly influenced by market conditions, unlike the government and higher education sectors.

Efficiency Near the Edge: Increasing the Energy Efficiency of FFTs on GPUs for Real-Time Edge Computing

Autoři
Adámek, K.; Novotný, J.; Thiyagalingam, J.; Armour, W.
Rok
2021
Publikováno
IEEE Access. 2021, 9 18167-18182. ISSN 2169-3536.
Typ
Článek
Anotace
The Square Kilometer Array (SKA) is an international initiative for developing the world's largest radio telescope with a total collecting area of over a million square meters. The scale of the operation, combined with the remote location of the telescope, requires the use of energy-efficient computational algorithms. This, along with the extreme data rates that will be produced by the SKA and the requirement for a real-time observing capability, necessitates in-situ data processing in an edge style computing solution. More generally, energy efficiency in the modern computing landscape is becoming of paramount concern. Whether it be the power budget that can limit some of the world's largest supercomputers, or the limited power available to the smallest Internet-of-Things devices. In this article, we study the impact of hardware frequency scaling on the energy consumption and execution time of the Fast Fourier Transform (FFT) on NVIDIA GPUs using the cuFFT library. The FFT is used in many areas of science and it is one of the key algorithms used in radio astronomy data processing pipelines. Through the use of frequency scaling, we show that we can lower the power consumption of the NVIDIA A100 GPU when computing the FFT by up to 47% compared to the boost clock frequency, with less than a 10% increase in the execution time. Furthermore, using one common core clock frequency for all tested FFT lengths, we show on average a 43% reduction in power consumption compared to the boost core clock frequency with an increase in the execution time still below 10%. We demonstrate how these results can be used to lower the power consumption of existing data processing pipelines. These savings, when considered over years of operation, can yield significant financial savings, but can also lead to a significant reduction of greenhouse gas emissions.

Emerging Technologies: Challenges and Opportunities for Logic Synthesis

Autoři
Bosio, A.; Cantan, M.; Marchand, C.; O'Connor, I.; Fišer, P.; Poittevin, A.; Traiola, M.
Rok
2021
Publikováno
Proceedings of 24th International Symposium on Design and Diagnostics of Electronic Circuits and Systems. Piscataway (New Jersey): IEEE, 2021. p. 93-98. ISBN 978-1-6654-3595-6.
Typ
Stať ve sborníku
Anotace
In computer engineering, logic synthesis is a process by which an abstract specification of desired circuit behavior is turned into a design implementation in terms of logic gates. Historically, logic synthesis was tightly related to the physical implementation of the logic gates. Nowadays, pushed by the forecasted end of Moore's law, several emerging technologies (e.g., nanodevices, optical computing, quantum computing) are candidates to either replace or co-exist with the \textit{de facto} standard CMOS technology. The main consequence of the rising of those emerging technologies is that the logic synthesis has to face new issues and, at the same time, exploits new opportunities. The goal of this paper is thus to present three emerging technologies (Vertical Nanowire Field Effect Transistors, Ferroelectric Transistors, and Memristors), how to use them to implement logic gates, and the main challenges and issues for the logic synthesis.

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Za obsah stránky zodpovídá: doc. Ing. Štěpán Starosta, Ph.D.