The goal of this diploma thesis is to create system for network data diagnostics based on detecting and correcting spelling errors. The system is intended to be used by network administrators as next diagnostics tool. As opposed to the primary use of detection and correction spelling error in common text, these methods are applied to network data, which are given by the user. Created system works with NetFlow data, pcap files or log files. Context is modeled with different created data categories. Dictionaries are used to verify the correctness of words, where each category uses its own. Finding a correction only according to the edit distance leads to many results and therefore a heuristic for evaluating candidates was proposed for selecting the right candidate. The created system was tested in terms of functionality and performance.

The subject of this thesis is the construction of one-way communication device. This device consists of the transmitter and the receiver, both of which are connected to its respective computer via USB interface. This device allows sending UTF-8 characters or files from one computer to another. Both computers are running a graphical user interface program. The core of a transmitter is a digital signal modulating laser diode. The receiver is made of photovoltaic panel with a resonance circuit and an amplifier. Communication between the electronics and the computer is driven by microcontrollers. Received messages are verified with one of multiple error detection algorithms, which can be selected by user in the utility program.

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第21391号 / 情博第677号 / 新制||情||117(附属図書館) / 京都大学大学院情報学研究科知能情報学専攻 / (主査)教授 河原 達也, 教授 黒橋 禎夫, 教授 壇辻 正剛, 准教授 南條 浩輝 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

Context: The volume of log files is massive as they contain vital information about the application’s behavior; they map out broad parts of the application, allowing us to understand how every component behaves, whether normally or abnormally. As a result, it is critical to examine the log files to see if the system is deviating from its usual path. Because they are so large, it is difficult for the developer to identify each and every error. So, to overcome this problem we developed a machine-learning model to detect types of errors in log files with minimal manual effort.  Objectives: The main objective is to discover errors in log files throughout the testing and production phases so that the application behaves properly. We intend to detect errors by training the module with relevant datasets and teaching the model to differentiate between the types of errors like error, debug, info, fail, etc. caused when the application is tested or operated during the production phase.  Methods: We employ machine learning techniques like SVM and multinomial naive Bayes as well as long-short-term memory (LSTM) networks, which are a sort of re-current neural network capable of learning order dependency in the prediction of sequences, which is appropriate for our use case. These techniques are used to de- termine whether errors such as assert, fail, error, and warning were generated. Then we used verdict generation machine learning techniques to generate the verdict from the error log messages.  Results: The results indicated that, instead of manually detecting errors, we can easily discover and fix them by integrating machine learning and classification methods, making it easier to move the application to production.  Conclusion: The results will assist developers in identifying the errors without having to manually examine the log file row by row. This approach has the potential to reduce the need for additional human efforts to examine log files for errors and can determine the type of error that occurred in the specific row that caused the application to diverge from its typical flow.

Space destined FPGA-based systems must employ redundancy techniques to account for the effects of upsets caused by radiated environments. Error detection techniques can be used to alert external systems to the presence of these upsets. Readback with compare is an error detection technique commonly employed in FPGA-based designs. This work introduces duplication with compare (DWC) as an automated on-line error detection technique that can be used as an alternative to readback with compare. This work also introduces a set of metrics that is used to quantify the effectiveness and coverage of this error detection technique. A tool is presented that automatically inserts duplication with compare into a user's design. Duplication with compare is shown to correctly detect over 99.9% of errors caused by configuration upsets at a hardware cost of approximately 2X. System designers can apply duplication with compare to designs using this tool to increase the reliability and availability of their systems while minimizing resource usage and power.

This dissertation presents a novel technique called duplicate with choose (DWCh) which is a modification of the fault detection technique duplicate with compare (DWC). DWCh adds a smart decider block to DWC that monitors the duplicated circuits and decides which circuit is fault free when a fault occurs. If chosen correctly, DWCh is able to mask faults at a lower cost than conventional techniques like TMR.This dissertation derives reliability expressions for DWCh showing that under ideal conditions its reliability exceeds the most commonly used fault masking technique for spacecraft, triple modular redundancy. For non-ideal conditions, DWCh provides a lower cost alternative than TMR but with lower reliability as well. Three types of DWCh smart deciders were developed for use with digital communications receivers. The first type used histograms as the statistical basis for the decider. The second type made use of moments for decision. The third type, although not generally applicable to other systems, used a signal common to communications receivers with excellent results. The communications receivers were subjected to hardware fault injection to gather datastreams affected by real world faults. The captured datastreams were used with Simulink models of the different deciders to quantify their performance and discover how a practical implementation of DWCh differs from the theoretical model. The increase in mean time to failure for DWCh when compared to simplex ranged from 20x to 130x depending on the specific smart decider tested.

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<p> Qualitative research exploring the development of coaching skills and knowledge suggests that this development begins with early athletic involvement. Unfortunately, the quantitative research exploring the association between athletic involvement and coaching abilities has not been able to identify a clear and causal relationship. This thesis investigates the relationship between previous athletic experience and current perceptual-cognitive coaching skills in novice and experienced gymnastics coaches using a quantitative experimental paradigm. Novice and experienced provincial and national level gymnastics coaches with and without experience competing at the provincial or national level participated in this study. In Experiment 1, twenty-three coaches completed a Knowledgebase Task and an Error Identification task. The data demonstrated that experienced coaches performed better on the knowledgebase task (F(1,19)=7.113, p. =0.016) and the Error Identification task than the novice coaches (F(1,18)=14.916, p. =0.001). While there were no performance differences on either task for experienced coaches with either significant or minimal athletic histories, novice coaches without previous athletic experience outperformed the novice coaches with previous athletic experience when asked to identify the initial error in a flawed gymnastics sequence (F(1,18)=5.338,p. =0.03). This counterintuitive result was further explored in Experiment 2. It was hypothesized that the nature of the knowledge on which the novice coaches rely differed based on their athletic histories. Specifically, novice coaches without athletic experience would rely to a greater degree on explicit knowledge whereas novice coaches with previous athletic experience would rely upon implicit knowledge acquired while an athlete. The results of Experiment 2 did not support this hypothesis. While this thesis was able to develop further insight into the relationship between previous athletic experience and current perceptual-cognitive coaching abilities, no clear and causal conclusions were drawn. Additional research is required to fully understand this complex and multi-dimensional relationship.</p> / Thesis / Master of Science (MSc)

Tato diplomová práce pojednává o využití algoritmu Dynamic Time Warping (DTW) pro automatické hodnocení výslovnosti anglického jazyka. Práce se zaměřuje na vylepšení již existující aplikace pro výuku výslovnosti, a to ve třech oblastech: uživatelské rozhraní, samotný algoritmus a korektivní zpětná vazba uživateli. První část se věnuje přehledu technik používaných v této oblasti, následně je představen nový design uživatelského rozhraní, popsán navržený systém a experimenty. Experimenty se zaměřují na problematiku detekce chyb na úrovni fonémů, na detekci chyb v primárním důrazu na úrovni slabik a na hodnocení intonace na úrovni slov. Všechny použité metody jsou navrženy tak, aby poskytovaly korektivní zpětnou vazbu uživateli. V poslední části je popsáno, jak byly všechny tři vylepšené oblasti aplikace otestovány.