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Client for the BlackDuck Protex Service
Knapek, Petr ; Zbořil, František (referee) ; Kočí, Radek (advisor)
Plagiarism and matching source codes are an increasing problem. The objective of this thesis is to evaluate Black Duck Software, Inc. service Protex and it's user interface. The next objective is to create a new, alternative client based on this evaluation, which will ease work process and offer automatization methods. Swing graphical library and multithreaded approach of Java programming language have been used in the implementation of a new client.
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Simulation of Complex Installment Schedule
Veselý, Michal ; Zbořil, František (referee) ; Samek, Jan (advisor)
The aim of this thesis is to examine relations from the field of financial mathematics concerning consumer and cash loans. Further objects of interest are various types of loan repaying, interest calculation and generating the installment schedule. The result of this mathemetical principles analysis is a complex simulator of installment schedule which was created for testing and configuration purposes in HCI company (Home Credit International).
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Social Networking for Questionnaire Survays
Huvar, Lukáš ; Zbořil, František (referee) ; Hrubý, Martin (advisor)
The aim of this work is to design and implement an application using an experimental library CloudKit. This application will serve as a social network for community submitters and questionnaire surveys respondents. Implemented social network is tested on a group of users who are involved in running the network. The benefit of this work is to test the Library CloudKit and its relevance for social networks.
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Traveling Salesman Problem
Šůstek, Martin ; Snášelová, Petra (referee) ; Zbořil, František (advisor)
This thesis is focused on modification of known principles ACO and GA to increase their performance. Thesis includes two new principles to solve TSP. One of them can be used as an initial population generator. The appendix contains the implementation of the application in Java. The description of this application is also part of the thesis. One part is devoted to optimization in order to make methods more efficient and produce shorter paths. In the end of the thesis are described experiments and their results with different number of places from 101 up to 3891.
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Comparison of Libraries of Artificial Neural Networks
Dohnal, Zdeněk ; Zbořil, František (referee) ; Dalecký, Štěpán (advisor)
This thesis is about comparison of libraries of artificial neural networks. Basic theory of neuron, neural networks and their learning algorithms are explained here. Multilayer perceptron, Self organizing map and Hopfield net are chosen for experiments. Criteria of comparison such as licence, community or last actualization are designed. Approximation of function, association and clustering are chosen as task for experiments. After that, there is implementation of applications using chosen libraries. At the end, result of comparison and experiment are evaluated.
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Agent Based Gameplaying System
Trutman, Michal ; Zbořil, František (referee) ; Král, Jiří (advisor)
This thesis deals with general game playing agent systems. On the contrary with common agents, which are designed only for a specified task or a game, general game playing agents have to be able to play basically any arbitrary game described in a formal declarative language. The biggest challenge is that the game rules are not known beforehand, which makes it impossible to use some optimizations or to make a good heuristic function. The thesis consists of a theoretical and a practical part. The first part introduces the field of general game playing agents, defines the Game Description Language and covers construction of heuristic evaluation functions and their integration within the Monte Carlo tree search algorithm. In the practical part, a general method of creating a new heuristic function is presented, which is later integrated into a proper agent, which is compared then with other systems.
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Fuzzy Neural Networks
González, Marek ; Rozman, Jaroslav (referee) ; Zbořil, František (advisor)
This thesis focuses on fuzzy neural networks. The combination of the fuzzy logic and artificial neural networks leads to the development of more robust systems. These systems are used in various field of the research, such as artificial intelligence, machine learning and control theory. First, we provide a quick overview of underlying neural networks and fuzzy systems to explain fundamental ideas that form the basis of the fields, and follow with the introduction of the fuzzy neural network theory, classification and application. Then we describe a design and a realization of the fuzzy associative memory, as an example of these systems. Finally, we benchmark the realization using the pattern recognition and control tasks. The results are evaluated and compared against existing systems.
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Extension of Behavioral Analysis of Network Traffic Focusing on Attack Detection
Teknős, Martin ; Zbořil, František (referee) ; Homoliak, Ivan (advisor)
This thesis is focused on network behavior analysis (NBA) designed to detect network attacks. The goal of the thesis is to increase detection accuracy of obfuscated network attacks. Methods and techniques used to detect network attacks and network traffic classification were presented first. Intrusion detection systems (IDS) in terms of their functionality and possible attacks on them are described next. This work also describes principles of selected attacks against IDS. Further, obfuscation methods which can be used to overcome NBA are suggested. The tool for automatic exploitation, attack obfuscation and collection of this network communication was designed and implemented. This tool was used for execution of network attacks. A dataset for experiments was obtained from collected network communications. Finally, achieved results emphasized requirement of training NBA models by obfuscated malicious network traffic.
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Automated Weight Tuning for Rule-Based Knowledge Bases
Valenta, Jan ; Pokorný, Miroslav (referee) ; Zbořil, František (referee) ; Jirsík, Václav (advisor)
This dissertation thesis introduces new methods of automated knowledge-base creation and tuning in information and expert systems. The thesis is divided in the two following parts. The first part is focused on the legacy expert system NPS32 developed at the Faculty of Electrical Engineering and Communication, Brno University of Technology. The mathematical base of the system is expression of the rule uncertainty using two values. Thus, it extends information capability of the knowledge-base by values of the absence of the information and conflict in the knowledge-base. The expert system has been supplemented by a learning algorithm. The learning algorithm sets weights of the rules in the knowledge base using differential evolution algorithm. It uses patterns acquired from an expert. The learning algorithm is only one-layer knowledge-bases limited. The thesis shows a formal proof that the mathematical base of the NPS32 expert system can not be used for gradient tuning of the weights in the multilayer knowledge-bases. The second part is focused on multilayer knowledge-base learning algorithm. The knowledge-base is based on a specific model of the rule with uncertainty factors. Uncertainty factors of the rule represents information impact ratio. Using a learning algorithm adjusting weights of every single rule in the knowledge base structure, the modified back propagation algorithm is used. The back propagation algorithm is modified for the given knowledge-base structure and rule model. For the purpose of testing and verifying the learning algorithm for knowledge-base tuning, the expert system RESLA has been developed in C#. With this expert system, the knowledge-base from medicine field, was created. The aim of this knowledge base is verify learning ability for complex knowledge-bases. The knowledge base represents heart malfunction diagnostic base on the acquired ECG (electrocardiogram) parameters. For the purpose of the comparison with already existing knowledge-basis, created by the expert and knowledge engineer, the expert system was compared with professionally designed knowledge-base from the field of agriculture. The knowledge-base represents system for suitable cultivar of winter wheat planting decision support. The presented algorithms speed up knowledge-base creation while keeping all advantages, which arise from using rules. Contrary to the existing solution based on neural network, the presented algorithms for knowledge-base weights tuning are faster and more simple, because it does not need rule extraction from another type of the knowledge representation.
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