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Vulnerability Reports Analysis and Management
Domány, Dušan ; Toropila, Daniel (advisor) ; Galgonek, Jakub (referee)
Various vulnerabilities in software products can often represent a significant security threat if they are discovered by malicious attackers. It is therefore important to identify these vulnerabilities and report their presence to responsible persons before they are exploited by malicious subjects. The number of security reports about discovered vulnerabilities in various software products has grown rapidly over the last decade. It is becoming more and more difficult to process all of the incoming reports manually. This work discusses various methods that can be used to automate several important processes in collecting and sorting the reports. The reports are analyzed in various ways, including techniques of text mining, and the results of the analysis are applied in form of practical implementation.
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Algorithms for Detection and Correction of Local Degradations in Digital Audio Signals
Kúdela, Jakub ; Toropila, Daniel (advisor) ; Petříček, Martin (referee)
Title: Algorithms for Detection and Correction of Local Degradations in Digital Audio Signals Author: Jakub K'udela Author's e-mail address: jakub.kudela@gmail.com Department: Department of Theoretical Computer Science and Mathematical Logic Thesis Supervisor: Mgr. Daniel Toropila Supervisor's e-mail address: daniel.toropila@mff.cuni.cz Abstract: Local degradations in audio signal are discontinuities in their wave- forms. They are caused by the nature of the recording process, or by aging of or damage to the recording medium. In many cases these discontinuities are un- wanted while listening, and so there exists a number of methods, whose aim is to restore degraded recordings. In the introduction, this thesis informs the reader about selected algorithms for detection and correction of local degradations in digital audio signals. One of the discussed algorithms is a custom aplication of artificial neural networks to the given problem. The implementation of selected algorithms and experiments are both parts of the thesis. The goal of the exper- iments is to both objectively and subjectively compare the performances of the selected algorithms. The thesis proposes a method for the objective evaluation of the quality of detection and correction, which, as will be shown, largely cor- responds to the subjective...
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Computer Poker
Kýpeť, Jakub ; Surynek, Pavel (advisor) ; Toropila, Daniel (referee)
The present work describes a design and an implementation of environment for playing Texas Hold'em poker, including design and implementation of a system that simulates a player of this game. This system consists of three developed strategies, using all resources described in the work. The various strategies that we have developed, we have tested on a few games. The results obtained from the tests are analyzed and then compared with our assumptions and expectations. This work also consists a description of existing programs that are about same topic and then comparation with our application.
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Planning algorithms and plan simulation in logistics domain
Štefan, Zdeněk ; Toropila, Daniel (advisor) ; Valla, Tomáš (referee)
The goal of this thesis is to compare some means of planning in the logistics domain.The aim was to compare the capabilities of those techniques among each other and against plans designed by humans.Because some planners do not return parallel plans,it is necessary to parallelize them.It is shown that some algorithms achieve good results in comparison to plans designed by hu- mans. However, due to their high time and memory consumptions they cannot be nowadays commonly used to solve more difficult tasks.
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Plánování osobní historie virtuálního agenta
Kučerová, Lucie ; Brom, Cyril (advisor) ; Toropila, Daniel (referee)
Episodic memory is an important component of "minds" of many longliving virtual agents, because equipping such an agent with his personal history increases his e ciency and believability. So far, research on episodic memory modeling in the context of these agents has focused mostly on producing the memory content on-line, that is, when the agent is being simulated. In this work, we address a complementary issue, automatic generation of the memory content off-line. We see a possible need of a tool for generating memories that anticipate the start of the simulation. Hence we created a complex design method enabling a designer to specify high-level requirements on an agent's history and use planning to automatically generate this history according to these requirements. We detail the structure of the high-level language used for the description of the requirements and the part of this method that concerns itself with the planning. In a set of experiments, we tested the performance of several planners on our task and we present here the results we gained.
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Compiling Planning Problems
Toropila, Daniel ; Barták, Roman (advisor) ; Chrpa, Lukáš (referee)
Constraint satisfaction techniques are used frequently for solving scheduling problems, but they are still seldom in AI planning. There exist several attempts to apply constraint satisfaction for solving AI planning problems, however, these techniques never became prevailing in planning and did not reach the success of, for example, SATbased planners. In this work we argue that the existing constraint models for classical AI planning are indeed not exploiting fully the power of constraint satisfaction and we propose their reformulation which significantly improves efficiency.
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Classical planning techniques
Sasák, Róbert ; Barták, Roman (advisor) ; Toropila, Daniel (referee)
Classical planning deals with nding a sequence of actions transferring the initial state of world into a desired goal state. This work surveys two classical planning techniques, forward and backward search. We implement both techniques in a form of software prototype using ve di erent search algorithms, in particular DFS, BFS, IDDFS, A*, WA*. By introducing additional heuristic we get family of 26 planners. We compare e ectivity of the planners on several domains from International Planning Competition. None of the planners is signi cantly better on all domains, however, in general, the planners based on forward search perform better.
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Generating of self-replicating cellular automata
Bardiovský, Vojtech ; Surynek, Pavel (advisor) ; Toropila, Daniel (referee)
The family of self-replicating cellular automata is interesting mainly for being able to demonstrate that even simple environments can make rise to structures capable of self-replication. Besides creating its own copy, a purposedly designed automaton can produce additional side patterns during its lifetime. The aim of the work is to create a cellular automata simulation environment that is flexible and fast, as some cellular automata become interesting only after thousands or millions of steps. The second aim of the work is to design and implement a generalisation of the Tempesti's loop using this environment. The outcome of the work is a generalisation that allows for automatized creation of rules and patterns for a given side pattern.
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Real-time Recognition of Typed Letters
Hámorník, Juraj ; Toropila, Daniel (advisor) ; Jančík, Pavel (referee)
Tablet PC devices are becoming more and more popular these days. These devices rarely have a hardware keyboard. Users input a text in two major ways. First one is a virtual keyboard, where users type each character individually and second one is handwrite recognition, where users write the whole words. Our method combines chars input method and handwriting. Hand written character that can contain multiple strokes, is converted into sequences of numbers. Based on this sequence we evaluate a written character. When we compare the default input method on operating system Windows 8 we can figure, that for advanced users, and for some languages our method could be a challange.
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Monte Carlo Techniques in Planning
Trunda, Otakar ; Barták, Roman (advisor) ; Toropila, Daniel (referee)
The Monte Carlo Tree Search (MCTS) algorithm has recently proved to be able to solve difficult problems in the field of optimization as well as game-playing. It has been able to address several problems that no conventional techniques have been able to solve efficiently. In this thesis we investigate possible ways to use MCTS in the field of planning and scheduling. We analyze the problem theoretically trying to identify possible difficulties when using MCTS in this field. We propose the solutions to these problems based on a modification of the algorithm and preprocessing the planning domain. We present the techniques we have developed for these tasks and we combine them into an applicable algorithm. We specialize the method for a specific kind of planning problems - the transportation problems. We compare our planner with other planning system.
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