Many historical documents are rich in genealogical facts. Extracting these facts by hand is tedious and almost impossible considering the hundreds of thousands of genealogically rich family-history books currently scanned and online. As one approach for helping to make the extraction feasible, we propose GreenFIE—a "Green" Form-based Information-Extraction tool which is "green" in the sense that it improves with use toward the goal of minimizing the cost of human labor while maintaining high extraction accuracy. Given a page in a historical document, the user's task is to fill out given forms with all facts on a page in a document called for by the forms (e.g. to collect the birth and death information, marriage information, and parent-child relationships for each person on the page). GreenFIE has a repository of extraction patterns that it applies to fill in forms. A user checks the correctness of GreenFIE's form filling, adds any missed facts, and fixes any mistakes. GreenFIE learns based on user feedback, adding new extraction rules to its repository. Ideally, GreenFIE improves as it proceeds so that it does most of the work, leaving little for the user to do other than confirm that its extraction is correct. We evaluate how well GreenFIE performs on family history books in terms of "greenness"—how much human labor diminishes during form filling, while simultaneously maintaining high accuracy.

Constructive methods for obtaining the regular grammar counterparts for some sub-classes of the context free grammars (cfg) have been investigated by many researchers. An important class of grammars for which this is always possible is the one-letter cfg. We show in this paper a new constructive method for transforming arbitrary one-letter cfg to an equivalent regular expression of star-height 0 or 1. Our new result is considerably simpler than a previous construction by Leiss, and we also propose a new normal form for a regular expression with single-star occurrence. Through an alphabet factorization theorem, we show how to go beyond the one-letter cfg in a straight-forward way. / Singapore-MIT Alliance (SMA)

In the past, information was stored more or less well-structured in database. Nowadays, a lot of information is presented in unstructured format. The management of and retrieval from such large vast of textual information has been a challenging issue for organizations or individuals. Information extraction is the process of extracting relevant data from semi-structured or unstructured documents and transforming them into structured representations. Many information extraction learning techniques have been proposed. However, they are ineffectiveness on unstructured documents. Thus, in the research, we proposed a new information extraction learning algorithm, called LIEF, that enhancing existing information extraction learning techniques. According to the empirical evaluations on news documents that are unstructured format, the LIEF algorithm proposed showed its capabilities in accuracy rate.

The goal of this thesis is to implement a tool that based on user defined properties can verify sequences of events in the traces of the program, or the log file. Properties are defined in extended regular expressions. The tool is able to verify parametric properties. User can define relations between parameters of events. Input of this tool is the definition of properties and constraints of parameters. Output of the tool is the report of violated properties with its sequences of events that caused the error.

The new General Data Protection Regulation (GDPR) Act will apply to all companies within the European Union after 25 May. This means stricter legal requirements for companies that in some way store personal data. The goal of this project is therefore to make it easier for companies to meet the new legal requirements. This by creating a tool that searches file systems and visually shows the user in a graphical user interface which files contain personal data. The tool uses Named entity recognition with the Linear chain conditional random field algorithm which is a type of supervised learning method in machine learning. This algorithm is used in the project to find names and addresses in files. The different models are trained with different parameters and the training is done using the stanford NER library in Java. The models are tested by a test file containing 45,000 words where the models themselves can predict all classes to the words in the file. The models are then compared with each other using the measurements of precision, recall and F-score to find the best model. The tool also uses Regular Expression to find emails, IP numbers, and social security numbers. The result of the final machine learning model shows that it does not find all names and addresses, but that can be improved by increasing exercise data. However, this is something that requires a more powerful computer than the one used in this project. An analysis of how the Swedish language is built would also need to be done to apply the most appropriate parameters for the training of the model. / Den nya lagen General data protection regulation (GDPR) började gälla för alla företag inom Europeiska unionen efter den 25 maj. Detta innebär att det blir strängare lagkrav för företag som på något sätt lagrar personuppgifter. Målet med detta projekt är därför att underlätta för företag att uppfylla de nya lagkraven. Detta genom att skapa ett verktyg som söker igenom filsystem och visuellt visar användaren i ett grafiskt användargränssnitt vilka filer som innehåller personuppgifter. Verktyget använder Named Entity Recognition med algoritmen Linear Chain Conditional Random Field som är en typ av ”supervised” learning metod inom maskininlärning. Denna algoritm används för att hitta namn och adresser i filer. De olika modellerna tränas med olika parametrar och träningen sker med hjälp av biblioteket Stanford NER i Java. Modellerna testas genom en testfil som innehåller 45 000 ord där modellerna själva får förutspå alla klasser till orden i filen. Modellerna jämförs sedan med varandra med hjälp av mätvärdena precision, recall och F-score för att hitta den bästa modellen. Verktyget använder även Regular expression för att hitta e- mails, IP-nummer och personnummer. Resultatet på den slutgiltiga maskininlärnings modellen visar att den inte hittar alla namn och adresser men att det är något som kan förbättras genom att öka träningsdata. Detta är dock något som kräver en kraftfullare dator än den som användes i detta projekt. En undersökning på hur det svenska språket är uppbyggt skulle även också behöva göras för att använda de lämpligaste parametrarna vid träningen av modellen.

O paradigma Network Function Virtualization (NFV) promete tornar as redes de computadores mais escaláveis e flexíveis, através do desacoplamento das funções de rede de hardware dedicado e fornecedor específico. No entanto, funções de rede computacionalmente intensivas podem ser difíceis de virtualizar sem degradação de desempenho. Neste contexto, Field-Programmable Gate Arrays (FPGAs) têm se mostrado uma boa opção para aceleração por hardware de funções de rede virtuais que requerem alta vazão, sem se desviar do conceito de uma infraestrutura NFV que visa alta flexibilidade. A avaliação de expressões regulares é um mecanismo importante e computacionalmente intensivo, usado para realizar Deep Packet Inpection, que pode ser acelerado por FPGA para atender aos requisitos de desempenho. Esta solução, no entanto, apresenta novos desafios em relação aos requisitos de confiabilidade. Particularmente para FPGAs baseados em SRAM, soft errors na memória de configuração são uma ameaça de confiabilidade significativa. Neste trabalho, apresentamos um mecanismo de tolerância a falhas abrangente para lidar com falhas de configuração na funcionalidade de módulos de avaliação de expressões regulares baseados em FPGA. Além disso, é introduzido um mecanismo de correção de erros que considera o posicionamento desses módulos no FPGA para reduzir o tempo de reparo do sistema, melhorando a confiabilidade e a disponibilidade. Os resultados experimentais mostram que a taxa de falha geral e o tempo de reparo do sistema podem ser reduzidos em 95% e 90%, respectivamente, com custos de área e performance admissíveis. / The Network Function Virtualization (NFV) paradigm promises to make computer networks more scalable and flexible by decoupling the network functions (NFs) from dedicated and vendor-specific hardware. However, network and compute intensive NFs may be difficult to virtualize without performance degradation. In this context, Field-Programmable Gate Arrays (FPGAs) have been shown to be a good option for hardware acceleration of virtual NFs that require high throughput, without deviating from the concept of an NFV infrastructure which aims at high flexibility. Regular expression matching is an important and compute intensive mechanism used to perform Deep Packet Inspection, which can be FPGA-accelerated to meet performance constraints. This solution, however, introduces new challenges regarding dependability requirements. Particularly for SRAM-based FPGAs, soft errors on the configuration memory are a significant dependability threat. In this work we present a comprehensive fault tolerance mechanism to deal with configuration faults on the functionality of FPGA-based regular expression matching engines. Moreover, a placement-aware scrubbing mechanism is introduced to reduce the system repair time, improving the system reliability and availability. Experimental results show that the overall failure rate and the system mean time to repair can be reduced in 95% and 90%, respectively, with manageable area and performance costs.

O paradigma Network Function Virtualization (NFV) promete tornar as redes de computadores mais escaláveis e flexíveis, através do desacoplamento das funções de rede de hardware dedicado e fornecedor específico. No entanto, funções de rede computacionalmente intensivas podem ser difíceis de virtualizar sem degradação de desempenho. Neste contexto, Field-Programmable Gate Arrays (FPGAs) têm se mostrado uma boa opção para aceleração por hardware de funções de rede virtuais que requerem alta vazão, sem se desviar do conceito de uma infraestrutura NFV que visa alta flexibilidade. A avaliação de expressões regulares é um mecanismo importante e computacionalmente intensivo, usado para realizar Deep Packet Inpection, que pode ser acelerado por FPGA para atender aos requisitos de desempenho. Esta solução, no entanto, apresenta novos desafios em relação aos requisitos de confiabilidade. Particularmente para FPGAs baseados em SRAM, soft errors na memória de configuração são uma ameaça de confiabilidade significativa. Neste trabalho, apresentamos um mecanismo de tolerância a falhas abrangente para lidar com falhas de configuração na funcionalidade de módulos de avaliação de expressões regulares baseados em FPGA. Além disso, é introduzido um mecanismo de correção de erros que considera o posicionamento desses módulos no FPGA para reduzir o tempo de reparo do sistema, melhorando a confiabilidade e a disponibilidade. Os resultados experimentais mostram que a taxa de falha geral e o tempo de reparo do sistema podem ser reduzidos em 95% e 90%, respectivamente, com custos de área e performance admissíveis. / The Network Function Virtualization (NFV) paradigm promises to make computer networks more scalable and flexible by decoupling the network functions (NFs) from dedicated and vendor-specific hardware. However, network and compute intensive NFs may be difficult to virtualize without performance degradation. In this context, Field-Programmable Gate Arrays (FPGAs) have been shown to be a good option for hardware acceleration of virtual NFs that require high throughput, without deviating from the concept of an NFV infrastructure which aims at high flexibility. Regular expression matching is an important and compute intensive mechanism used to perform Deep Packet Inspection, which can be FPGA-accelerated to meet performance constraints. This solution, however, introduces new challenges regarding dependability requirements. Particularly for SRAM-based FPGAs, soft errors on the configuration memory are a significant dependability threat. In this work we present a comprehensive fault tolerance mechanism to deal with configuration faults on the functionality of FPGA-based regular expression matching engines. Moreover, a placement-aware scrubbing mechanism is introduced to reduce the system repair time, improving the system reliability and availability. Experimental results show that the overall failure rate and the system mean time to repair can be reduced in 95% and 90%, respectively, with manageable area and performance costs.

O paradigma Network Function Virtualization (NFV) promete tornar as redes de computadores mais escaláveis e flexíveis, através do desacoplamento das funções de rede de hardware dedicado e fornecedor específico. No entanto, funções de rede computacionalmente intensivas podem ser difíceis de virtualizar sem degradação de desempenho. Neste contexto, Field-Programmable Gate Arrays (FPGAs) têm se mostrado uma boa opção para aceleração por hardware de funções de rede virtuais que requerem alta vazão, sem se desviar do conceito de uma infraestrutura NFV que visa alta flexibilidade. A avaliação de expressões regulares é um mecanismo importante e computacionalmente intensivo, usado para realizar Deep Packet Inpection, que pode ser acelerado por FPGA para atender aos requisitos de desempenho. Esta solução, no entanto, apresenta novos desafios em relação aos requisitos de confiabilidade. Particularmente para FPGAs baseados em SRAM, soft errors na memória de configuração são uma ameaça de confiabilidade significativa. Neste trabalho, apresentamos um mecanismo de tolerância a falhas abrangente para lidar com falhas de configuração na funcionalidade de módulos de avaliação de expressões regulares baseados em FPGA. Além disso, é introduzido um mecanismo de correção de erros que considera o posicionamento desses módulos no FPGA para reduzir o tempo de reparo do sistema, melhorando a confiabilidade e a disponibilidade. Os resultados experimentais mostram que a taxa de falha geral e o tempo de reparo do sistema podem ser reduzidos em 95% e 90%, respectivamente, com custos de área e performance admissíveis. / The Network Function Virtualization (NFV) paradigm promises to make computer networks more scalable and flexible by decoupling the network functions (NFs) from dedicated and vendor-specific hardware. However, network and compute intensive NFs may be difficult to virtualize without performance degradation. In this context, Field-Programmable Gate Arrays (FPGAs) have been shown to be a good option for hardware acceleration of virtual NFs that require high throughput, without deviating from the concept of an NFV infrastructure which aims at high flexibility. Regular expression matching is an important and compute intensive mechanism used to perform Deep Packet Inspection, which can be FPGA-accelerated to meet performance constraints. This solution, however, introduces new challenges regarding dependability requirements. Particularly for SRAM-based FPGAs, soft errors on the configuration memory are a significant dependability threat. In this work we present a comprehensive fault tolerance mechanism to deal with configuration faults on the functionality of FPGA-based regular expression matching engines. Moreover, a placement-aware scrubbing mechanism is introduced to reduce the system repair time, improving the system reliability and availability. Experimental results show that the overall failure rate and the system mean time to repair can be reduced in 95% and 90%, respectively, with manageable area and performance costs.

Spam se typicky nevyskytuje ve formě samostatných zpráv, ale často bývá sdružován do takzvaných kampaní. Ty bývají automaticky generovány pomocí šablon. Díky tomu jsou jednotlivé zprávy sémanticky, ale ne syntakticky, ekvivalentní. Cílem práce je navrhnout algoritmus schopný z množiny zpráv jedné kampaně zpětně extrahovat šablonu, ze které tyto zprávy byly generovány. Práce se zaměřuje na spam v SMS komunikaci, ale navržené postupy jsou dostatečně obecné pro širší použití. Algoritmus je postaven na metodě zarovnávání dvou sekvencí, používané v bioinformatice pro nalezení podobných oblastí proteinových řetězců. Výstupem je regulární výraz popisující šablonu dané kampaně. Součástí řešení je také nástroj pro vizualizaci šablony pomocí HTML.Řešení bylo ověřeno na přibližně třech stovkách skutečných kampaní z celého světa. V naprosté většině případů je poskytnutý výsledek postačující pro identifikaci kampaně.

This thesis deals with the pattern matching methods based on finite automata and describes their optimizations. It presents a methodology for the measurement of reliability of pattern matching methods, by comparing their results to the results of the PCRE library. Experiments were conducted for a finite automaton with perfect hashing and faulty transition table. Finally, the resulting reliability evaluation of the algorithm is shown and possible solutions of the identified problems are proposed.