A parallel algorithm for approximating the silhouette using a ball tree

Rok
2023
Publikováno
Journal of Parallel and Distributed Computing. 2023, 173 115-125. ISSN 0743-7315.
Typ
Článek
Anotace
Clustering is widely used in many scientific fields. The contribution of enumerating the value of the silhouette is twofold: firstly it can help choosing a suitable cluster count and secondly it can be used to evaluate the quality of a clustering. Enumerating the silhouette exactly is an extremely time-consuming task, especially in big data applications; it is therefore common to approximate its value. This article presents an efficient shared-memory parallel algorithm for approximating the silhouette, which uses a ball tree. The process of initialising the ball tree and enumerating the silhouette are fully parallelised using the OpenMP API. The results of our experiments show that the proposed parallel algorithm substantially increases the speed of computing the silhouette whilst retaining necessary precision for real-world applications.

Adapting the Size of Artificial Neural Networks Using Dynamic Auto-Sizing

Autoři
Cahlík, V.; Kordík, P.; Čepek, M.
Rok
2023
Publikováno
IEEE 17th International Conference on Computer Science and Information Technologies. Dortmund: IEEE, 2023. p. 592-596. ISBN 979-8-3503-3431-9.
Typ
Stať ve sborníku
Anotace
We introduce dynamic auto-sizing, a novel approach to training artificial neural networks which allows the models to automatically adapt their size to the problem domain. The size of the models can be further controlled during the learning process by modifying the applied strength of regularization. The ability of dynamic auto-sizing models to expand or shrink their hidden layers is achieved by periodically growing and pruning entire units such as neurons or filters. For this purpose, we introduce weighted L1 regularization, a novel regularization method for inducing structured sparsity. Besides analyzing the behavior of dynamic auto-sizing, we evaluate predictive performance of models trained using the method and show that such models can provide a predictive advantage over traditional approaches.

Consistency check of automatic pipeline measurements of quasar redshifts with Bayesian convolutional networks

Rok
2023
Publikováno
Astronomical Data Analysis Software and Systems XXXII. San Francisco: Astronomical Society of the Pacific, 2023.
Typ
Stať ve sborníku
Anotace
Spectroscopic redshifts of quasars are important inputs for constructing many cosmological models. Redshift measurement is generally considered to be a straightforward task performed by automatic pipelines based on template matching. Due to the millions of spectra delivered by surveys of SDSS or LAMOST telescopes, it is impossible to verify all redshift measurements of automatic pipelines by a human visual inspection. However, the pipeline results are still taken as the "ground truth" for further statistical inferences. Nevertheless, because of the similarity of patterns of quasar emission lines in different spectral ranges, an optimal match may be found for a completely different template position, causing severe errors in the measured redshift. For example, it may easily happen that a faint emission star with a noisy spectrum is identified as a high redshift quasar and vice versa. We show such examples discovered by the consistency check of redshift measurements of the SDSS pipeline and redshift predictions of a regression Bayesian convolutional network. The network is trained on a large amount of human-inspected redshifts and predicts redshifts together with their predictive uncertainties. Therefore, it can also identify cases where predictions are uncertain and thus require human visual inspection.

DeCrypto: Finding Cryptocurrency Miners on ISP networks

Autoři
Plný, R.; Hynek, K.; Čejka, T.
Rok
2023
Publikováno
Secure IT Systems. Cham: Springer, 2023. ISSN 0302-9743. ISBN 978-3-031-22294-8.
Typ
Stať ve sborníku
Anotace
With the rising popularity of cryptocurrencies and the increasing value of the whole industry, people are incentivized to join and earn revenues by cryptomining — using computational resources for cryptocurrency transaction verification. Nevertheless, there is an increasing number of abusive cryptomining cases, and it is reported that “coin miner malware” grew by more than 4000% in 2018. In this work, we analyzed the cryptominer network communication and proposed the DeCrypto system that can detect and report mining on high-speed 100 Gbps backbone Internet lines with millions of users. The detector uses the concept of heterogeneous weak-indication detectors (Machine-Learning-based, domain-based, and payload-based) that work together and create a robust and accurate detector with an extremely low false-positive rate. The detector was implemented and evaluated on a real nationwide high-speed network and proved efficient in a real-world deployment.

Domain Dependent Parameter Setting in SAT Solver Using Machine Learning Techniques

Rok
2023
Publikováno
Agents and Artificial Intelligence 14th International Conference, ICAART 2022 Virtual Event, February 3–5, 2022 Revised Selected Papers. Cham: Springer International Publishing AG, 2023. p. 169-200. ISSN 0302-9743. ISBN 978-3-031-22952-7.
Typ
Stať ve sborníku
Anotace
We address the problem of variable and truth-value choice in modern search-based Boolean satisfiability (SAT) solvers depending on the problem domain. The SAT problem is the task to determine truth-value assignment for variables of a given Boolean formula under which the formula evaluates to true. The SAT problem is often used as a canonical representation of combinatorial problems in many domains of computer science ranging from artificial intelligence to software engineering. Modern complete search-based SAT solvers represent a universal problem solving tool which often provide higher efficiency than ad-hoc direct solving approaches. Many efficient variable and truth-value selection heuristics were devised. Heuristics can usually be fine tuned by single or multiple numerical parameters prior to executing the search process over the concrete SAT instance. In this paper we present a machine learning approach that predicts the parameters of heuristic from the underlying structure of a graph derived from the input SAT instance. Using this approach we effectively fine tune the SAT solver for specific problem domain.

Flexing infinite frameworks with applications to braced Penrose tilings

Autoři
Dewar, S.; Legerský, J.
Rok
2023
Publikováno
Discrete Applied Mathematics. 2023, 324 1-17. ISSN 0166-218X.
Typ
Článek
Anotace
A planar framework – a graph together with a map of its vertices to the plane – is flexible if it allows a continuous deformation preserving the distances between adjacent vertices. Extending a recent previous result, we prove that a connected graph with a countable vertex set can be realized as a flexible framework if and only if it has a so-called NAC-coloring. The tools developed to prove this result are then applied to frameworks where every 4-cycle is a parallelogram, and countably infinite graphs with n-fold rotational symmetry. With this, we determine a simple combinatorial characterization that determines whether the 1-skeleton of a Penrose rhombus tiling with a given set of braced rhombi will have a flexible motion, and also whether the motion will preserve 5-fold rotational symmetry.

Offline evaluation of the serendipity in recommendation systems

Autoři
Pastukhov, D.; Kuznetsov, S.; Vančura, V.; Kordík, P.
Rok
2023
Publikováno
IEEE 17th International Conference on Computer Science and Information Technologies. Dortmund: IEEE, 2023. p. 597-601. ISBN 979-8-3503-3431-9.
Typ
Stať ve sborníku
Anotace
Offline optimization of recommender systems is a difficult task. Popular optimization criteria such as RMSE, Recall, and NDCG do not correlate much with online performance, especially when the recommendation algorithm is largely different from the one used to generate the offline data. An exciting direction of research to mitigate this problem is to use more robust optimization criteria. Serendipity is reported to be a promising proxy. However, more variants exist, and it is unclear whether they can be used as a single criterion to optimize. This paper analyzes how serendipity relates to other optimization criteria for three different recommendation algorithms. Based on our findings, we propose to modify the way serendipity is computed. We conduct experiments using three collaborative filtering algorithms: K-Nearest Neighbors, Matrix Factorization, and Embarrassingly Shallow Autoencoder (EASE). We also employ and evaluate the ensemble learning approach and analyze the importance of the individual components of serendipity.

On balanced sequences and their critical exponent

Autoři
Dvořáková, L.; Dolce, F.; Pelantová, E.
Rok
2023
Publikováno
Theoretical Computer Science. 2023, 939 18-47. ISSN 0304-3975.
Typ
Článek
Anotace
We study aperiodic balanced sequences over finite alphabets. A sequence v of this type is fully characterised by a Sturmian sequence u and two constant gap sequences y and y'. We show that the language of v is eventually dendric and we focus on return words to its factors. We deduce a method computing critical exponent and asymptotic critical exponent of balanced sequences provided the associated Sturmian sequence u has a quadratic slope. The method is based on looking for the shortest return words to bispecial factors in v. We illustrate our method on several examples, in particular we confirm a conjecture of Rampersad, Shallit and Vandomme that two specific sequences have the least critical exponent among all balanced sequences over 9- resp. 10-letter alphabets.

Overview of ParaCell package for indexing in powder diffraction

Autoři
Šimeček, I.; Rohlíček, J.; Zaloga, A.
Rok
2023
Publikováno
Journal of Applied Crystallography. 2023, 56(1), ISSN 1600-5767.
Typ
Článek
Anotace
This article describes the crystallography package ParaCell that integrates several indexing methods. All methods share the basic infrastructure of the program that uses graphical processing units (GPU) to evaluate the correctness of the unit cell. The individually implemented indexing methods in the program are presented along with a comparison with other indexing software. The success and time requirements were tested on several datasets, including orthorhombic, monoclinic and triclinic examples.

Polynomial kernels for tracking shortest paths

Autoři
Blažej, V.; Choudhary, P.; Knop, D.; Křišťan, J.; Suchý, O.; Valla, T.
Rok
2023
Publikováno
Information Processing Letters. 2023, 179(1), ISSN 1872-6119.
Typ
Článek
Anotace
Given an undirected graph G = (V , E), vertices s,t ∈ V , and an integer k, Tracking Shortest Paths requires deciding whether there exists a set of k vertices T ⊆ V such that for any two distinct shortest paths between s and t, say P1 and P2, we have T ∩ V (P1) != T ∩ V (P2). In this paper, we give the first polynomial size kernel for the problem. Specifically we show the existence of a kernel with O(k2) vertices and edges in general graphs and a kernel with O(k) vertices and edges in planar graphs for the Tracking Paths in DAG problem. This problem admits a polynomial parameter transformation to Tracking Shortest Paths, and this implies a kernel with O(k4) vertices and edges for Tracking Shortest Paths in general graphs and a kernel with O(k2) vertices and edges in planar graphs. Based on the above we also give a single exponential algorithm for Tracking Shortest Paths in planar graphs.

Prototype of Interactive Visualisation Tool for Bayesian Active Deep Learning

Rok
2023
Publikováno
Astronomy Data Analysis Software and Systems XXXI. San Francisco: Astronomical Society of the Pacific, 2023.
Typ
Stať ve sborníku
Anotace
n the era of big data in astronomy, we need to develop methods to analyse the data. One such method is Bayesian active deep learning (synergy of Bayesian convolutional neural networks and active learning). To improve the method’s performance, we have developed a prototype of an interactive visualisation tool for a selection of an informative (contains data with high predictive uncertainty, is diverse, but not redundant) data subsample for labelling by a human expert. The tool takes as input a sample of data with the highest predictive uncertainty. These data are projected to 2-D with a dimensionality reduction technique. We visualise the projected data in an interactive scatter plot and allow a human expert to label a selected subsample of data. With this tool, she or he can select a correct subsample with all the previously mentioned characteristics. This should lower the total amount of data labelled because the Bayesian model’s performance will improve faster than when the data are selected automatically.

A Comprehensive Survey on the Non-Invasive Passive Side-Channel Analysis

Rok
2022
Publikováno
Sensors. 2022, 22(21), ISSN 1424-8220.
Typ
Článek
Anotace
Side-channel analysis has become a widely recognized threat to the security of cryptographic implementations. Different side-channel attacks, as well as countermeasures, have been proposed in the literature. Such attacks pose a severe threat to both hardware and software cryptographic implementations, especially in the IoT environment where the attacker may easily gain physical access to a device, leaving it vulnerable to tampering. In this paper, we provide a comprehensive survey regarding the non-invasive passive side-channel analysis. We describe both non-profiled and profiled attacks, related security metrics, countermeasures against such attacks, and leakage-assessment methodologies, as available in the literature of more than twenty years of research.

A Design Space Exploration Framework for Memristor-Based Crossbar Architecture

Autoři
Barbareschi, M.; Bosio, A.; O'Connor, I.; Fišer, P.; Traiola, M.
Rok
2022
Publikováno
Proceedings of the 2022 25th International Symposium on Design and Diagnostics of Electronic Circuits and Systems (DDECS). Piscataway: IEEE, 2022. p. 38-43. ISSN 2473-2117. ISBN 978-1-6654-9431-1.
Typ
Stať ve sborníku
Anotace
In the literature, there are few studies describing how to implement Boolean logic functions as a memristor-based crossbar architecture and some solutions have been actually proposed targeting back-end synthesis. However, there is a lack of methodologies and tools for the synthesis automation. %More in detail, what is missing is a methodology able to optimize a given Boolean logic function for the memristor-based implementation. The main goal of this paper is to perform a Design Space Exploration (DSE) in order to analyze and compare the impact of the most used optimization algorithms on a memristor-based crossbar architecture. %The synthesized circuits are quantified in terms of area, energy consumption, and performance. The results carried out on 102 circuits lead us to identify the best optimization approach, in terms of area/energy/delay. The presented results can also be considered as a reference (benchmarking) for comparing future work.

A fair experimental evaluation of distance correlation side-channel distinguisher

Rok
2022
Publikováno
Proceedings of the 11th Mediterranean Conference on Embedded Computing (MECO 2022). Institute of Electrical and Electronics Engineers, Inc., 2022. p. 110-113. ISSN 2637-9511. ISBN 978-1-6654-6828-2.
Typ
Stať ve sborníku
Anotace
Side-channel attacks pose a severe threat to crypto graphic implementations, allowing the attacker to recover secret information based on physical observations of the cryptographic device. Correlation Power Analysis is considered to be one of the most powerful attacks in the non-profiled scenario. In this paper, we consider the distance/Brownian correlation instead of the traditionally used Pearson coefficient. We give a fair comparison of our novel approach attacking AES on three different FPGA platforms and we discuss the distance correlation potential in the context of side-channel analysis.

A Survey of Guidelines and Best Practices for the Generation, Interlinking, Publication, and Validation of Linguistic Linked Data

Autoři
Khan, A.F.; Chiarcos, C.; Declerck, T.; Buono, M.P.; Dojčinovski, M.; Gracia, J.; Oleskeviciene, G.V.; Gifu, D.
Rok
2022
Publikováno
Proceedings of LDL 2022. Paris: ELRA, 2022. p. 1-9.
Typ
Stať ve sborníku
Anotace
This article discusses a survey carried out within the NexusLinguarum COST Action which aimed to give an overview of existing guidelines (GLs) and best practices (BPs) in linguistic linked data. In particular it focused on four core tasks in the production/publication of linked data: generation, interlinking, publication, and validation. We discuss the importance of GLs and BPs for LLD before describing the survey and its results in full. Finally we offer a number of directions for future work in order to address the findings of the survey.

Ab initio View of Emergent Symplectic Symmetry and Its Crutial Role in Nuclear Dynamics

Autoři
Dytrych, T.; Launey, K.D.; Draayer, J.P.; Langr, D.
Rok
2022
Publikováno
Bulgarian Journal of Physics. 2022, 49(1), 37-46. ISSN 1310-0157.
Typ
Článek
Anotace
Results of large-scale first principle nuclear structure studies using the symmetry-adapted no-core shell model are reported. It is shown that nuclei up through the intermediate-mass region display highly regular and ubiquitous patterns of dominant nuclear shapes that vibrate and rotate. This emergent structure is tied to an approximate symplectic Sp (3, R) symmetry, and it is shown to determine dominant features of low-lying states, even in close-to-spherical nuclear states without any recognizable rotational properties.

Accepted Tutorials at The Web Conference 2022

Autoři
Tommasini, R.; Roy, S.B.; Wang, X.; Dojčinovski, M.
Rok
2022
Publikováno
Companion Proceedings of the Web Conference 2022. New York: Association for Computing Machinery, 2022. p. 391-399. ISBN 978-1-4503-9130-6.
Typ
Stať ve sborníku
Anotace
This paper summarizes the content of the 20 tutorials that have been given at The Web Conference 2022: 85% of these tutorials are lecture style, and 15% of these are hands on.

Approximate arithmetic for modern neural networks and FPGAs

Rok
2022
Publikováno
Proceedings of the 11th Mediterranean Conference on Embedded Computing (MECO 2022). Institute of Electrical and Electronics Engineers, Inc., 2022. p. 357-360. ISSN 2637-9511. ISBN 978-1-6654-6828-2.
Typ
Stať ve sborníku
Anotace
Approximate arithmetic is a very important approach for implementing neural networks in embedded hardware. The requirements of real-time applications with respect to the size of deep neural networks force designers to simplify the neural processing elements. Not only the reduction of precision of model parameters to a few bits, but also the use of approximate arithmetic increases computational power and saves on-chip resources beyond exact computation. Since it was shown that linearly approximated functions are suitable for implementing neural networks in hardware, FPGAs have improved. So we decided to re-implement the processing element on a modern FPGA and to present implementation results regarding speed and resource consumption. A neural processing element based on linearly approximated functions was implemented in Vivado and tested on an xc7 FPGA. The results show that the architecture saves significant resources and a clock frequency above 100 MHz can be achieved in pipelined design.

Automatic Detection and Decryption of AES Using Dynamic Analysis

Autoři
Kokeš, J.; Matějka, J.; Lórencz, R.
Rok
2022
Publikováno
SN Computer Science. 2022, 2022 ISSN 2662-995X.
Typ
Článek
Anotace
In this paper we propose a set of algorithms that can automatically detect the use of AES and automatically recover both the encryption key and the plaintext, assuming that we can control the code flow of the encrypting program, e.g., when an application is performing encryption without the user’s permission. The first algorithm 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 naïve 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. We also discuss the options of recovering the same data when hardware-assisted AES implementations on Intel-compatible architectures are used.

Balancing the Spread of Two Opinions in Sparse Social Networks (Student Abstract)

Rok
2022
Publikováno
Proceedings of the 36th AAAI Conference on Artificial Intelligence. Menlo Park: AAAI Press, 2022. p. 12987-12988. ISSN 2159-5399. ISBN 978-1-57735-876-3.
Typ
Stať ve sborníku
Anotace
We propose a new discrete model for simultaneously spreading two opinions within a social network inspired by the famous Target Set Selection problem. We are given a social network, a seed-set of agents for each opinion, and two thresholds per agent. The first threshold represents the willingness of an agent to adopt an opinion if she has no opinion at all, while the second threshold states the readiness to acquire a second opinion. The goal is to add as few agents as possible to the initial seed-sets such that, once the process started with these seed-set stabilises, each agent has either both opinions or none. We perform an initial study of its computational complexity. It is not surprising that the problem is NP-hard even in quite restricted settings. Therefore, we investigate the complexity of the problem from the parameterized point-of-view with special focus on sparse networks, which appears often in practice. Among other things, we show that the proposed problem is in the FPT complexity class if we parameterize by the vertex cover number of the underlying graph.

Filtr

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