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Object recognition using 3D convolutional neural networks
Moravec, Jaroslav ; Lokoč, Jakub (advisor) ; Straka, Milan (referee)
Title: Object recognition using 3D convolutional neural networks Author: Jaroslav Moravec Department: Department of Software Engineering Supervisor: RNDr. Jakub Lokoč, Ph.D., Department of Software Engineering Abstract: With the fast development of laser and sensor technologies, it has become easy to scan a real-world object and save it in a digital format into a persistent database. With the rising number of scanned 3D objects, data man- agement and retrieval methods become necessary. For various retrieval tasks, effective retrieval models are required. In our work, we focus on effective classifi- cation and similarity search. The investigated approach is based on convolutional neural networks representing a machine learning method that boomed in recent years. We have designed and trained several architectures of 3D convolutional neural networks and tested them on state-of-the-art benchmark 3D datasets for 3D object recognition and retrieval. We were also able to show that the trained features on one dataset can be then used to predict class labels on another 3D dataset. Keywords: Object recognition, 3D convolution, neural networks
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Detection of malignant melanoma in histological sample using deep neural networks
Frey, Adam ; Lokoč, Jakub (advisor) ; Straka, Milan (referee)
The aim of this thesis is to create a classification method for detection of ma- lignant melanoma in high-resolution digital images. Deep convolutional neural networks were used for this task. At first, a short overview of malignant melanoma and ways to detect it is presented. Deep convolutional neural networks are also introduced with a special attention given to models used further in this work. Several ways to generate samples from the provided histological images are discussed, and several experiments are evaluated to decide how to maximize the accuracy of employed classification methods. The thesis then focuses on several neural network structures used for image classification and their possible utiliza- tion for the given task. The emphasis is laid on the transfer learning, a method used for modifying already trained models for different tasks. This method is then used for training several classifiers. Further on, several methods for the visualization of model results are discussed with some of them implemented. The experiments show promising results on par with other studies dealing with similar problems. Several possibilities for further development are listed in the conclusion.
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Natural Language Correction
Náplava, Jakub ; Straka, Milan (advisor) ; Straňák, Pavel (referee)
The goal of this thesis is to explore the area of natural language correction and to design and implement neural network models for a range of tasks ranging from general grammar correction to the specific task of diacritization. The thesis opens with a description of existing approaches to natural language correction. Existing datasets are reviewed and two new datasets are introduced: a manually annotated dataset for grammatical error correction based on CzeSL (Czech as a Second Language) and an automatically created spelling correction dataset. The main part of the thesis then presents design and implementation of three models, and evaluates them on several natural language correction datasets. In comparison to existing statistical systems, the proposed models learn all knowledge from training data; therefore, they do not require an error model or a candidate generation mechanism to be manually set, neither they need any additional language information such as a part of speech tags. Our models significantly outperform existing systems on the diacritization task. Considering the spelling and basic grammar correction tasks for Czech, our models achieve the best results for two out of the three datasets. Finally, considering the general grammatical correction for English, our models achieve results which are...
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Named Entity Recognition and Linking
Taufer, Pavel ; Straka, Milan (advisor) ; Kliegr, Tomáš (referee)
The goal of this master thesis is to design and implement a named entity recognition and linking algorithm. A part of this goal is to propose and create a knowledge base that will be used in the algorithm. Because of the limited amount of data for languages other than English, we want to be able to train our method on one language, and then transfer the learned parameters to other languages (that do not have enough training data). The thesis consists of description of available knowledge bases, existing methods and design and implementation of our own knowledge base and entity linking method. Our method achieves state of the art result on a few variants of the AIDA CoNLL-YAGO dataset. The method also obtains comparable results on a sample of Czech annotated data from the PDT dataset using the parameters trained on the English CoNLL dataset. Powered by TCPDF (www.tcpdf.org)
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Off-line connection search on Google Android platform
Křepelka, Michal ; Bojar, Ondřej (advisor) ; Straka, Milan (referee)
This thesis discusses the connection search in public transit without permanent connection to the server, that would do the time-consuming calculations. For this purpose, we use the Transfer Pattern method running on Google Android platform. We demonstrate to the reader some of the most common graphs used for connection search in public transit and subsequently the procedure how to formalize timetables as such graphs. Further we describe principles of Transfer Patterns, a way how to compute them from timetable graph and how to store them in SQLite database on Android device. On such pre-computed data, we can very quickly and efficiently find the optimal connection even on relatively performance-limited Android device.
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Functional Data Stuctures and Algorithms
Straka, Milan ; Dvořák, Zdeněk (advisor) ; Koucký, Michal (referee) ; Brodal, Gerth (referee)
Title: Functional Data Structures and Algorithms Author: Milan Straka Institute: Computer Science Institute of Charles University Supervisor of the doctoral thesis: doc. Mgr. Zdeněk Dvořák, Ph.D, Computer Science Institute of Charles University Abstract: Functional programming is a well established programming paradigm and is becoming increasingly popular, even in industrial and commercial appli- cations. Data structures used in functional languages are principally persistent, that is, they preserve previous versions of themselves when modified. The goal of this work is to broaden the theory of persistent data structures and devise efficient implementations of data structures to be used in functional languages. Arrays are without any question the most frequently used data structure. Despite being conceptually very simple, no persistent array with constant time access operation exists. We describe a simplified implementation of a fully per- sistent array with asymptotically optimal amortized complexity Θ(log log n) and especially a nearly optimal worst-case implementation. Additionally, we show how to effectively perform a garbage collection on a persistent array. The most efficient data structures are not necessarily based on asymptotically best structures. On that account, we also focus on data structure...
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Qudratic field based cryptography
Straka, Milan ; Žemlička, Jan (referee) ; Stanovský, David (advisor)
Imaginary quadratic fields were first suggested as a setting for public-key cryptography by Buchmann and Williams already in 1988 and more cryptographic schemes followed. Although the resulting protocols are currently not as efficient as those based on elliptic curves, they are comparable to schemes based on RSA and, moreover, their security is believed to be independent of other widely-used protocols including RSA, DSA and elliptic curve cryptography. This work gathers present results in the field of quadratic cryptography. It recapitulates the algebraic theory needed to work with the class group of imaginary quadratic fields. Then it investigates algorithms of class group operations, both asymptotically and practically effective. It also analyses feasible cryptographic schemes and attacks upon them. A library implementing described cryptographic schemes is a part of this work.
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Persistent data structures
Kupec, Martin ; Straka, Milan (referee) ; Mareš, Martin (advisor)
This thesis discusses persistent data structures, that is structures which preserve their own history. We focus on pointer-based structures, where it is possible to reach both full and partial persistence in constant amortized time and space per operation. Persistent arrays are also discussed, but the existence of optimal persistent arrays remains an open problem. We also include specific applications of the general techniques and also examples of use of persistent data structures.
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Qudratic field based cryptography
Straka, Milan ; Stanovský, David (advisor)
Imaginary quadratic fields were first suggested as a setting for public-key cryptography by Buchmann and Williams already in 1988 and more cryptographic schemes followed. Although the resulting protocols are currently not as efficient as those based on elliptic curves, they are comparable to schemes based on RSA and, moreover, their security is believed to be independent of other widely-used protocols including RSA, DSA and elliptic curve cryptography. This work gathers present results in the field of quadratic cryptography. It recapitulates the algebraic theory needed to work with the class group of imaginary quadratic fields. Then it investigates algorithms of class group operations, both asymptotically and practically effective. It also analyses feasible cryptographic schemes and attacks upon them. A library implementing described cryptographic schemes is a part of this work.
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Factorization of polynomials over finite fields
Straka, Milan ; Stanovský, David (referee) ; Žemlička, Jan (advisor)
Nazcv prace: Faktorizace polynoinu nad konccnynii telesy Autor: Milan Straka Katcdra (ustav): Katcdra algebry Vedouci bakalarske prace: Mgr. Jan Zcmlicka, Ph.D. E-mail vedouciho: Jan.Zemlicka((hnff. cuni.cz Abstrakt: Cilem prace je prozkoumat problem rozkladu polynomn nad konecnym telc- scm na soucin ircducibilnich polynoinu. PopHanim nekolika algoritmu hledaji- cich tento rozklad se ukaze, ze tento problem je vzdy fcsitclny v polynornialnim case vzhleclem kc stupni polynomu a poctu prvku konecneho telcsa. U jeduoho z algoritnm je po])sana implenientace s vclnii clobrou asymptotic- kou casovou slozito.sti O(nLylD log c/}, kdc i\. jc stupen rozkladaneho polynuinn nad telesem « q prvky. Program pouzivajiei jcdnodnssi, ale prakticky rychlcjsi variantu tohoto algoritnm jc soucasti ])racc. Klicova slova: faktorizace, kouecna telesa, polynoniy, algoritmns Title: Factoring polynomials over finite fields Author: Milan Straka Department: Department of Algebra Supervisor: Mgr. Jan Zemlicka, Ph.D. Supervisor's e-mail address: Jan. Zcirilicka@mJJ.cum.cz Abstract: The goal of this work is to present the problem of the decomposition of a polyno- mial over a finite field into a product of irreducible polynomials. By describing algorithms solving this problem, we show that the decomposition can always be found in...
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