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Retargetable Analysis of Machine Code
Křoustek, Jakub ; Janoušek, Jan (oponent) ; Návrat,, Pavol (oponent) ; Kolář, Dušan (vedoucí práce)
Program analysis is a computer-science methodology whose task is to analyse the behavior of a given program. The methods of program analysis can also be used in other methodologies such as reverse engineering, re-engineering, code migration, etc. In this thesis, we focus on program analysis of a machine-code and we address the limitations of a nowadays approaches by proposing novel methods of a fast and accurate retargetable analysis (i.e. they are designed to be independent of a particular target platform). We focus on two types of analysis - dynamic analysis (i.e. run-time analysis) and static analysis (i.e. analysing application without its execution). The contribution of this thesis within the dynamic analysis lays in the extension and enhancement of existing methods and their implementation as a retargetable debugger and two types of a retargetable translated simulator. Within the static analysis, we present a concept and implementation of a retargetable decompiler that performs a program transformation from a machine code into a human-readable form of representation. All of these tools are based on several novel methods defined by the author. According to our experimental results and users feed-back, all of the proposed tools are at least fully competitive to existing solutions, while outperforming these solutions in several ways.
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Packet Classification Algorithms
Puš, Viktor ; Lhotka,, Ladislav (oponent) ; Dvořák, Václav (vedoucí práce)
This thesis deals with packet classification in computer networks. Classification is the key task in many networking devices, most notably packet filters - firewalls. This thesis therefore concerns the area of computer security. The thesis is focused on high-speed networks with the bandwidth of 100 Gb/s and beyond. General-purpose processors can not be used in such cases, because their performance is not sufficient. Therefore, specialized hardware is used, mainly ASICs and FPGAs. Many packet classification algorithms designed for hardware implementation were presented, yet these approaches are not ready for very high-speed networks. This thesis addresses the design of new high-speed packet classification algorithms, targeted for the implementation in dedicated hardware. The algorithm that decomposes the problem into several easier sub-problems is proposed. The first subproblem is the longest prefix match (LPM) operation, which is used also in IP packet routing. As the LPM algorithms with sufficient speed have already been published, they can be used in out context. The following subproblem is mapping the prefixes to the rule numbers. This is where the thesis brings innovation by using a specifically constructed hash function. This hash function allows the mapping to be done in constant time and requires only one memory with narrow data bus. The algorithm throughput can be determined analytically and is independent on the number of rules or the network traffic characteristics. With the use of available parts the throughput of 266 million packets per second can be achieved. Additional three algorithms (PFCA, PCCA, MSPCCA) that follow in this thesis are designed to lower the memory requirements of the first one without compromising the speed. The second algorithm lowers the memory size by 11 % to 96 %, depending on the rule set. The disadvantage of low stability is removed by the third algorithm, which reduces the memory requirements by 31 % to 84 %, compared to the first one. The fourth algorithm combines the third one with the older approach and thanks to the use of several techniques lowers the memory requirements by 73 % to 99 %.
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Harnessing Forest Automata for Verification of Heap Manipulating Programs
Šimáček, Jiří ; Abdulla, Parosh (oponent) ; Křetínský, Mojmír (oponent) ; Vojnar, Tomáš (vedoucí práce)
This work addresses verification of infinite-state systems, more specifically, verification of programs manipulating complex dynamic linked data structures. Many different approaches emerged to date, but none of them provides a~sufficiently robust solution which would succeed in all possible scenarios appearing in practice. Therefore, in this work, we propose a new approach which aims at improving the current state of the art in several dimensions. Our approach is based on using tree automata, but it is also partially inspired by some ideas taken from the methods based on separation logic. Apart from that, we also present multiple advancements within the implementation of various tree automata operations, crucial for our verification method to succeed in practice. Namely, we provide an optimised algorithm for computing simulations over labelled transition systems which then translates into more efficient computation of simulations over tree automata. We also give a new algorithm for checking inclusion over tree automata, and we provide experimental evaluation demonstrating
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Acceleration of Object Detection Using Classifiers
Juránek, Roman ; Kälviäinen, Heikki (oponent) ; Sojka, Eduard (oponent) ; Zemčík, Pavel (vedoucí práce)
Detection of objects in computer vision is a complex task. One of most popular and well explored approaches is the use of statistical classifiers and scanning windows. In this approach, classifiers learned by AdaBoost algorithm (or some modification) are often used as they achieve low error rates, high detection rates and they are suitable for detection in real-time applications. Object detection run-time which uses such classifiers can be implemented by various methods and properties of underlying architecture can be used for speed-up of the detection. For the purpose of acceleration, graphics hardware, multi-core architectures, SIMD or other means can be used. The detection is often implemented on programmable hardware. The contribution of this thesis is to introduce an optimization technique which enhances object detection performance with respect to an user defined cost function. The optimization balances computations of previously learned classifiers between two or more run-time implementations in order to minimize the cost function. The optimization method is verified on a basic example -- division of a classifier to a pre-processing unit implemented in FPGA, and a post-processing unit in standard PC.
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Optimization of Gaussian Mixture Subspace Models and Related Scoring Algorithms in Speaker Verification
Glembek, Ondřej ; Brummer, Niko (oponent) ; Campbell,, William (oponent) ; Burget, Lukáš (vedoucí práce)
This thesis deals with Gaussian Mixture Subspace Modeling in automatic speaker recognition. The thesis consists of three parts. In the first part, Joint Factor Analysis (JFA) scoring methods are studied. The methods differ mainly in how they deal with the channel of the tested utterance. The general JFA likelihood function is investigated and the methods are compared both in terms of accuracy and speed. It was found that linear approximation of the log-likelihood function gives comparable results to the full log-likelihood evaluation while simplyfing the formula and dramatically reducing the computation speed. In the second part, i-vector extraction is studied and two simplification methods are proposed. The motivation for this part was to allow for using the state-of-the-art technique on small scale devices and to setup a simple discriminative-training system. It is shown that, for long utterances, while sacrificing the accuracy, we can get very fast and compact i-vector systems. On a short-utterance(5-second) task, the results of the simplified systems are comparable to the full i-vector extraction. The third part deals with discriminative training in automatic speaker recognition. Previous work in the field is summarized and---based on the knowledge from the earlier chapters of this work---discriminative training of the i-vector extractor parameters is proposed. It is shown that discriminative re-training of the i-vector extractor can improve the system if the initial estimation is computed using the generative approach.
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Network-wide Security Analysis
de Silva, Hidda Marakkala Gayan Ruchika ; Šafařík,, Jiří (oponent) ; Šlapal, Josef (oponent) ; Švéda, Miroslav (vedoucí práce)
The objective of the research is to model and analyze the effects of dynamic routing protocols. The thesis addresses the analysis of service reachability, configurations, routing and security filters on dynamic networks in the event of device or link failures. The research contains two main sections, namely, modeling and analysis. First section consists of modeling of network topology, protocol behaviors, device configurations and filters. In the modeling, graph algorithms, routing redistribution theory, relational algebra and temporal logics were used. For the analysis of reachability, a modified topology table was introduced. This is a unique centralized table for a given network and invariant for network states. For the analysis of configurations, a constraint-based analysis was developed by using XSD Prolog. Routing and redistribution were analyzed by using routing information bases and for analyzing the filtering rules, a SAT-based decision procedure was incorporated. A part of the analysis was integrated to a simulation tool at OMNeT++ environment. There are several innovations introduced in this thesis. Filtering network graph, modified topology table, general state to reduce the state space, modeling devices as filtering nodes and constraint-based analysis are the key innovations. Abstract network graph, forwarding device model and redistribution with routing information are extensions of the existing research. Finally, it can be concluded that this thesis discusses novel approaches, modeling methods and analysis techniques in the area of dynamic networks. Integration of these methods into a simulation tool will be a very demanding product for the network designers and the administrators.
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Relational Verification of Programs with Integer Data
Konečný, Filip ; Bouajjani, Ahmed (oponent) ; Jančar, Petr (oponent) ; Vojnar, Tomáš (vedoucí práce)
This work presents novel methods for verification of reachability and termination properties of programs that manipulate unbounded integer data. Most of these methods are based on acceleration techniques which compute transitive closures of program loops. We first present an algorithm that accelerates several classes of integer relations and show that the new method performs up to four orders of magnitude better than the previous ones. On the theoretical side, our framework provides a common solution to the acceleration problem by proving that the considered classes of relations are periodic. Subsequently, we introduce a semi-algorithmic reachability analysis technique that tracks relations between variables of integer programs and applies the proposed acceleration algorithm to compute summaries of procedures in a modular way. Next, we present an alternative approach to reachability analysis that integrates predicate abstraction with our acceleration techniques to increase the likelihood of convergence of the algorithm. We evaluate these algorithms and show that they can handle a number of complex integer programs where previous approaches failed. Finally, we study the termination problem for several classes of program loops and show that it is decidable. Moreover, for some of these classes, we design a polynomial time algorithm that computes the exact set of program configurations from which nonterminating runs exist. We further integrate this algorithm into a semi-algorithmic method that analyzes termination of integer programs, and show that the resulting technique can verify termination properties of several non-trivial integer programs.
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Evolutionary Approach to Synthesis and Optimization of Ordinary and Polymorphic Circuits
Gajda, Zbyšek ; Schmidt, Jan (oponent) ; Zelinka,, Ivan (oponent) ; Sekanina, Lukáš (vedoucí práce)
This thesis deals with the evolutionary design and optimization of ordinary and polymorphic circuits. New extensions of Cartesian Genetic Programming (CGP) that allow reducing of the computational time and obtaining more compact circuits are proposed and evaluated. Second part of the thesis is focused on new methods for synthesis of polymorphic circuits. Proposed methods, based on polymorphic binary decision diagrams and polymorphic multiplexing, extend the ordinary circuit representations with the aim of including polymorphic gates. In order to reduce the number of gates in circuits synthesized using proposed methods, an evolutionary optimization based on CGP is implemented and evaluated. The implementations of polymorphic circuits optimized by CGP represent the best known solutions if the number of gates is considered as the target criterion.
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On-line Data Analysis Based on Visual Codebooks
Beran, Vítězslav ; Honec, Jozef (oponent) ; Sojka, Eduard (oponent) ; Zemčík, Pavel (vedoucí práce)
This work introduces the new adaptable method for on-line video searching in real-time based on visual codebook. The new method addresses the high computational efficiency and retrieval performance when used on on-line data. The method originates in procedures utilized by static visual codebook techniques. These standard procedures are modified to be able to adapt to changing data. The procedures, that improve the new method adaptability, are dynamic inverse document frequency, adaptable visual codebook and flowing inverted index. The developed adaptable method was evaluated and the presented results show how the adaptable method outperforms the static approaches when evaluating on the video searching tasks. The new adaptable method is based on introduced flowing window concept that defines the ways of selection of data, both for system adaptation and for processing. Together with the concept, the mathematical background is defined to find the best configuration when applying the concept to some new method. The practical application of the adaptable method is particularly in the video processing systems where significant changes of the data domain, unknown in advance, is expected. The method is applicable in embedded systems monitoring and analyzing the broadcasted TV on-line signals in real-time.
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Physically-based Modeling and Simulation
Dvořák, Radim ; Racek, Stanislav (oponent) ; Šujanský,, Milan (oponent) ; Zbořil, František (vedoucí práce)
The thesis deals with the modeling of air pollution transportation and dispersion processes in the atmosphere, more precisely with the numerical approaches to solve such models. The modeling of air pollution has a great importance for prediction of the contaminations and it helps with understanding of the process and with elimination of its consequences. The models which are described by partial differential equations, namely advection-diffusion equations, and thus they can be solved by numerous analytical/numerical methods are in the scope of the thesis. In particular, well known method of lines (MoL) and several models based on it together with the possibility to accelerate the computation are studied in the first half of the work. It is shown that MoL approach is still suitable for many concrete models and it has a great potential for parallelization on graphics cards. Quite young ELLAM method and its application to solved atmospheric advection-diffusion equations is the second objective. A concrete form of ELLAM method and its proposed adaptation approaches are evaluated and it is shown that it overcomes the current state of the art methods in many cases.
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