Analysis of grammatical evolutionary approaches to regular expression induction

Compiler Design is one of the core courses of Computer Science and Engineering. Syntax Analyzer or Parser is important phase in designing the compiler. So while designing this phase of the compiler, students must have thorough knowledge about the types of parser such as LL(1), SLR, Canonical LR and LALR. In the parser phase, the parsing table is built for given Context Free Grammar (CFG). While implementing this parsing phase in laboratory session, two Free Open Source Software (FOSS) - JFLAP and Parsing Emulator, are considered to explain the types of parser and building the parsing table for the CFG using these parser types. JFLAP is software for experimenting with formal languages topics including nondeterministic finite automata, nondeterministic pushdown automata, multi-tape Turing machines, several types of grammars, parsing, and Lsystems. So this tool is considered for parsing phase of Compiler. JFLAP tool is used to explain the steps to be followed to solve the given problem using the parsing technique. Parsing simulator is a software which implements the parsing table for the Context Free Grammars in tool. This simulator is used to generate parsing table (LL1, SLR, LR, and LALR). Parsing Emulator tool is used for practicing the problem statement given in the tool. In current study, use of FOSS such as JFLAP and Parsing Emulator is considered for teaching parser types to improve problem solving ability. The Learning Objectives (LOs) of this study are: To build the parsing table for given CFG using LL(1) and SLR parser (LO1) and to parse the given string using LL(1) and SLR parser (LO2). The research questions for this study are – whether the use of FOSS improves the problem solving ability of the students? and whether gender bias is there in education? Two groups post-test method is considered to check the effectiveness of this use of FOSS in learning the parser types. Also the students’ perception about this method is also considered. The result shows that the students’ problem solving ability of experimental group is improved as compared to the control group irrespective of gender. Keywords- FOSS (Free Open Source Software) JFLAP, Parsing Emulator, Bloom’s Taxonomy, Post-test, t-Test.

In this thesis, various artificial recurrent neural network models are investigated for the problem of deriving grammar rules from a finite set of example sentences. A general discrete network framework and its corresponding learning algorithm are presented and studied in detail in learning three different types of grammars. The first type of grammars considered is regular grammars. Experiments with conventional analog recurrent networks in learning regular grammars are presented to demonstrate the unstable behavior of such networks in processing very long strings after training. A new network structure to force recurrent networks to learn stable states by discretizing the internal feedback signals is constructed. For training such discrete networks a learning rule is applied. As an extension to the discrete network model, an external discrete stack is added to accommodate the inference of context-free grammars. A composite error function is devised to deal with various situations during learning. The pseudo-gradient method is also extended to train such a network to learn context-free grammars with the added option of operating on the external stack. Another extension to the discrete network structure is made for the purpose of learning probabilistic finite state grammars. The network consists of a discrete portion which is intended to represent the structure of the grammar, and an analog portion which is intended to represent the transition probabilities. Two criteria for the network to verify the correctness of its solution during training are proposed. Theoretical analysis of the necessary and sufficient conditions for the correct solution is presented. Experimental results show that the discrete network models have similar capabilities in learning various grammars as their analog counterparts, and have the advantage of being provably stable.

To evaluate the outcomes and elucidate the failure factors for trabeculectomy with mitomycin C (MMC) in Southwest Chinese patients. A retrospective correlational study was conducted on the glaucomatous patients who underwent initial trabeculectomy with MMC in Southwest Hospital and had been followed up for 1-3y. A complete success for surgery is defined as a postoperative intraocular pressure (IOP) >5 and ≤21 mm Hg and 20% reduction of IOP compared to preoperative, without IOP-lowering medications. A qualified success for surgery is defined as the abovementioned postoperative IOP with or without IOP-lowering medications. The primary outcomes were IOP, the number of IOP-lowering medications, and cumulative success rate. The secondary outcomes included best corrected visual acuity (BCVA), mean deviation (MD) of visual field, major complications, and risk factors for surgical failure. A total of 325 eyes of 261 glaucomatous patients had been included in our study. Both the mean IOP and the number of IOP-lowering medications were significantly decreased from 32.9±12.0 to 16.4±5.7 mm Hg (P<0.0001) and 3.0±0.9 to 0.9±1.0 (P<0.0001), respectively, at the last visit. The cumulative complete success rate and qualified success rate were 77.8% and 92.0% at 1-year follow-up, and 47.2% and 77.7% at 3-year follow up. There were no significant differences in surgical outcomes between primary angle-closure glaucoma (PACG) and primary open angle glaucoma (POAG). In PACG patients, the success rates of trabeculectomy were comparable with those of phacotrabeculectomy. Hypertension (HR=1.904, P=0.011), encapsulated bleb (HR=2.756, P<0.001), and more preoperative topical medications (HR=2.475, P=0.008) were risk factors for surgical failure. The qualified success rate of trabeculectomy with MMC in glaucomatous patients in the cohort is 92.0% at 1-year, and 77.7% at 3-year follow up. Hypertension, encapsulated bleb, and more preoperative topical medications are associated with surgical failure.

Phacogoniotomy versus phacotrabeculectomy for advanced primary angle-closure glaucoma with cataract: A randomized non-inferiority trial

Parsing with Derivatives (PwD) is an elegant approach to parsing context-free grammars (CFGs). It takes the equational theory behind Brzozowski's derivative for regular expressions and augments that theory with laziness, memoization, and fixed points. The result is a simple parser for arbitrary CFGs. Although recent work improved the performance of PwD, it remains inefficient due to the algorithm repeatedly traversing some parts of the grammar. In this functional pearl, we show how to avoid this inefficiency by suspending the state of the traversal in a zipper. When subsequent derivatives are taken, we can resume the traversal from where we left off without retraversing already traversed parts of the grammar. However, the original zipper is designed for use with trees, and we want to parse CFGs. CFGs can include shared regions, cycles, and choices between alternates, which makes them incompatible with the traditional tree model for zippers. This paper develops a generalization of zippers to properly handle these additional features. Just as PwD generalized Brzozowski's derivatives from regular expressions to CFGs, we generalize Huet's zippers from trees to CFGs. Abstract The resulting parsing algorithm is concise and efficient: it takes only 31 lines of OCaml code to implement the derivative function but performs 6,500 times faster than the original PwD and 3.24 times faster than the optimized implementation of PwD.

We tackle the problem of analyzing filter and sanitizer programs remotely, i.e. given only the ability to query the targeted program and observe the output. We focus on two important and widely used program classes: regular expression (RE) filters and string sanitizers. We demonstrate that existing tools from machine learning that are available for analyzing RE filters, namely automata learning algorithms, require a very large number of queries in order to infer real life RE filters. Motivated by this, we develop the first algorithm that infers symbolic representations of automata in the standard membership/equivalence query model. We show that our algorithm provides an improvement of x15 times in the number of queries required to learn real life XSS and SQL filters of popular web application firewall systems such as mod-security and PHPIDS. % Active learning algorithms require the usage of an equivalence oracle, i.e. an oracle that tests the equivalence of a hypothesis with the target machine. We show that when the goal is to audit a target filter with respect to a set of attack strings from a context free grammar, i.e. find an attack or infer that none exists, we can use the attack grammar to implement the equivalence oracle with a single query to the filter. Our construction finds on average 90% of the target filter states when no attack exists and is very effective in finding attacks when they are present. For the case of string sanitizers, we show that existing algorithms for inferring sanitizers modelled as Mealy Machines are not only inefficient, but lack the expressive power to be able to infer real life sanitizers. We design two novel extensions to existing algorithms that allow one to infer sanitizers represented as single-valued transducers. Our algorithms are able to infer many common sanitizer functions such as HTML encoders and decoders. Furthermore, we design an algorithm to convert the inferred models into BEK programs, which allows for further applications such as cross checking different sanitizer implementations and cross compiling sanitizers into different languages supported by the BEK backend. We showcase the power of our techniques by utilizing our black-box inference algorithms to perform an equivalence checking between different HTML encoders including the encoders from Twitter, Facebook and Microsoft Outlook email, for which no implementation is publicly available.

The definition of “grammar form” introduced in [CG] makes it possible to state and prove results about various types of grammars in a uniform way. Among questions naturally formalizable in this framework are many about the complexity or efficiency of grammars of different kinds. Grammar forms provide a reasonable way of considering the totality of other forms we might use, and so answering the question with both upper and lower bound results.

Learning TOC – begins is a web application which is useful for Learning TOC (Theory of Computation). It covers hypothesis of the basic points with cases and it likewise has Exercise segment in which client can check different speculations for all intents and purposes. It likewise creates drawing of different cases. So the client can learn it adequately and additionally quick. Client can build FA of the string without anyone else and print or fare it for his task work. It has the office of Test to check his score and readiness work. So it is extremely valuable for client as exam arrangement. This Web Application is valuable for the educators and understudies and additionally different clients which are has a place with the Computer Science field. The fundamental reason for this web application is to pick up everything outwardly and graphically. It covers the listed topics given below- Regular Expression, Finite Automata, Context Free Grammar, Push Down Automata, Turing Machine, Exercise of the topics, Mock test.

Surgical management of glaucoma in Fuchs uveitis syndrome: Trabeculectomy or Ahmed glaucoma valve

We present a functional approach to parsing unrestricted context-free grammars based on Brzozowski's derivative of regular expressions. If we consider context-free grammars as recursive regular expressions, Brzozowski's equational theory extends without modification to context-free grammars (and it generalizes to parser combinators). The supporting actors in this story are three concepts familiar to functional programmers - laziness, memoization and fixed points; these allow Brzozowski's original equations to be transliterated into purely functional code in about 30 lines spread over three functions. Yet, this almost impossibly brief implementation has a drawback: its performance is sour - in both theory and practice. The culprit? Each derivative can double the size of a grammar, and with it, the cost of the next derivative. Fortunately, much of the new structure inflicted by the derivative is either dead on arrival, or it dies after the very next derivative. To eliminate it, we once again exploit laziness and memoization to transliterate an equational theory that prunes such debris into working code. Thanks to this compaction, parsing times become reasonable in practice. We equip the functional programmer with two equational theories that, when combined, make for an abbreviated understanding and implementation of a system for parsing context-free languages.

Generation of Pattern-Matching Algorithms by Extended Regular Expressions

Many important entity types in web documents, such as dates, times, email addresses, and course numbers, follow or closely resemble patterns that can be described by Regular Expressions (REs). Due to a vast diversity of web documents and ways in which they are being generated, even seemingly straightforward tasks such as identifying mentions of date in a document become very challenging. It is reasonable to claim that it is impossible to create a RE that is capable of identifying such entities from web documents with perfect precision and recall. Rather than abandoning REs as a go-to approach for entity detection, this paper explores ways to combine the expressive power of REs, ability of deep learning to learn from large data, and human-in-the loop approach into a new integrated framework for entity identification from web data. The framework starts by creating or collecting the existing REs for a particular type of an entity. Those REs are then used over a large document corpus to collect weak labels for the entity mentions and a neural network is trained to predict those RE-generated weak labels. Finally, a human expert is asked to label a small set of documents and the neural network is fine tuned on those documents. The experimental evaluation on several entity identification problems shows that the proposed framework achieves impressive accuracy, while requiring very modest human effort.

BackgroundMalaria causes significant morbidity in Malawi, with an estimated 5 million cases in 2014. Artemether-lumefantrine (AL) and artesunate-amodiaquine (ASAQ) are the first- and second-line treatments for uncomplicated malaria, respectively, but emerging resistance threatens their efficacy. In order to understand whether AL and ASAQ remain efficacious for the treatment of uncomplicated Plasmodium falciparum malaria in Malawi, a therapeutic efficacy trial was conducted.MethodsDuring March–July 2014, febrile children aged 6–59 months with microscopy-confirmed uncomplicated P. falciparum malaria (1000–200,000 parasites/μL) were enrolled in a 28-day randomized in vivo efficacy trial at three sites: one each in northern (Karonga), central (Nkhotakota) and southern (Machinga) Malawi. The study was powered to estimate site-specific efficacy for AL and overall efficacy for ASAQ, with 3:1 randomization to AL or ASAQ. Blood was collected for malaria microscopy and molecular testing on days 0–3, 7, 14, 21, and 28. Recrudescence and reinfection were differentiated using polymerase chain reaction (PCR) genotyping of merozoite surface protein. The primary outcome was the PCR-corrected day 28 Kaplan–Meier cumulative success rate.ResultsA total of 452 children were enrolled; 303/338 (89 %) and 98/114 (86 %) reached a study endpoint in AL and ASAQ arms, respectively. All treatment failures occurred after day 3. The day 28 uncorrected cumulative success rate was 97.1 % (95 % confidence interval [CI]: 93.9–100 %) for ASAQ and 76.8 % (95 % CI 72.1–81.5 %) for AL, with 82.5 % (95 % CI 75.4–89.7 %), 69 % (95 % CI 59.9–78.1 %), and 78.2 % (95 % CI 70.2–86.3 %) success in the northern, central, and southern regions, respectively. The day 28 PCR-corrected cumulative success rate was 99 % (95 % CI 97.2–100 %) in the ASAQ arm and 99.3 % (95 % CI 98.3–100 %) in the AL arm, with 98–100 % efficacy in each site.ConclusionsAs evidenced by the day 28 PCR-corrected cumulative success rates, both AL and ASAQ remain efficacious treatments for uncomplicated malaria in Malawi. The lower uncorrected efficacy in the AL arm compared to ASAQ may be explained by the shorter half-life of lumefantrine (3–6 days) compared to amodiaquine (9–18 days). The high reinfection rate suggests that there is a continued need to scale-up effective malaria prevention interventions.

Over the past several years we have created or borrowed algorithms for combinatorial pattern matching and pattern discovery on sequences [2] and trees.In matching problems, given a pattern, a set of data objects and a distance metric, we find the distance between the pattern and one or more data objects. In discovery problems by contrast, given a set of objects, a metric, and a distance, we seek a pattern that matches many of those objects within the given distance. (So, discovery is a lot like data mining.) Our toolkit performs both matching and discovery with current targeted applications in molecular biology and document comparison.

We consider the problem of restoring regular expressions from good examples. We describe a natural learning algorithm for obtaining a “plausible” regular expression from one example. The algorithm is based on finding the longest substring which can be matched by some part of the so far obtained expression. We believe that the algorithm to a certain extent mimics humans guessing regular expressions from the same sort of examples. We show that for regular expressions of bounded length successful learning takes time linear in the length of the example, provided that the example is “good”. Under certain natural restrictions the run-time of the learning algorithm is polynomial also in unsuccessful cases. In the end we discuss the computer experiment of learning regular expressions via the described algorithm, showing that the proposed learning method is quite practical.