Recursive Descent Research Articles

Backwards-reachability for cooperating multi-pushdown systems

A cooperating multi-pushdown system consists of a tuple of pushdown systems that can delegate the execution of recursive procedures to sub-tuples; control returns to the calling tuple once all sub-tuples finished their task. This allows the concurrent execution since disjoint sub-tuples can perform their task independently. Because of the concrete form of recursive descent into sub-tuples, the content of the multi-pushdown does not form an arbitrary tuple of words, but can be understood as a Mazurkiewicz trace. For such systems, we prove that the backwards reachability relation efficiently preserves recognizability, generalizing a result and proof technique by Bouajjani et al. for single-pushdown systems. It follows that the reachability relation is decidable for cooperating multi-pushdown systems in polynomial time and the same holds, e.g., for safety and liveness properties given by recognizable sets of configurations.

Interval Parsing Grammars for File Format Parsing

File formats specify how data is encoded for persistent storage. They cannot be formalized as context-free grammars since their specifications include context-sensitive patterns such as the random access pattern and the type-length-value pattern. We propose a new grammar mechanism called Interval Parsing Grammars IPGs) for file format specifications. An IPG attaches to every nonterminal/terminal an interval, which specifies the range of input the nonterminal/terminal consumes. By connecting intervals and attributes, the context-sensitive patterns in file formats can be well handled. In this paper, we formalize IPGs' syntax as well as its semantics, and its semantics naturally leads to a parser generator that generates a recursive-descent parser from an IPG. In general, IPGs are declarative, modular, and enable termination checking. We have used IPGs to specify a number of file formats including ZIP, ELF, GIF, PE, and part of PDF; we have also evaluated the performance of the generated parsers.

Survey on Various Syntax Analyzer Tools

Abstract: Syntax analysis forms the second phase of a compiler. Syntax Analyzer basically takes the input of tokens from the lexical analyzer and parses the source code according to the production rules to detect errors in the code. The syntax analyzer gives an output in the form of a parse tree. The syntax analysis techniques can be classified as top-down parsing and bottom-up parsing. These categories can be further subdivided into recursive descent, LL (1), operator precedence, LR (0), SLR (1), CLR (1) and LALR (1) respectively. Various parser generators can generate parsers of these types which have been studied and analyzed in this paper such as Beaver, Tatoo, APG etc. This paper provides an overview of all these tools with respective to their working, advantages and features. These syntax analyzer tools can be used for different purposes according to the user.

ATTRIBUTE EVALUATION METHODS IN A TOP-DOWN TABLE-DRIVEN PARSER

The article outlines methods for efficient computation of attributes in a table-driven top-down parsing process. This methods are based on the introduction of a stack of attributes into the parser. Semantic actions using this stack are defined by one of three methods. The first method is based on the fact that two parsers: table-driven and recursive descent parsers have the same use of depth first traversal. Therefore, the mechanisms for computing attributes in recursive descent are also applicable in a table-driven parser. Then the attribute stack will act like a frame stack of function calls. The second method assumes that the attribute stack is the memory of an abstract machine. For the source language of the translator, the structural-operational semantics is specified, which puts the translation result in line with the language constructions. The semantic actions of the translation scheme are implemented as analyzing and applying rules of an abstract machine built on the basis of structural-operational semantics. The third method assumes that an abstract syntax tree in postfix form will be formed in the attribute stack. Semantic actions for the attribute stack are built in such a way that the expressions formed on the stack are evaluated as far as possible. The article shows that the application of the three proposed methods to the scheme of translation of arithmetic expressions gives the same result. The proposed methods can significantly reduce the amount of memory used and make the design of the translator more understandable.

Gerber File Parsing for Conversion to Bitmap Image–The VINCI7D Case Study

The technological market is increasingly evolving as evidenced by the innovative and streamlined manufacturing processes. Printed Circuit Boards (PCB) are widely employed in the electronics fabrication industry, resorting to the Gerber open standard format to transfer the manufacturing data. The Gerber format describes not only metadata related to the manufacturing process but also the PCB image. To be able to map the electronic circuit pattern to be printed, a parser to convert Gerber files into a bitmap image is required. The current literature as well as available Gerber viewers and libraries showed limitations mainly in the Gerber format support, focusing only on a subset of commands. In this work, the development of a recursive descent approach for parsing Gerber files is described, outlining its interpretation and the renderization of 2D bitmap images. All the defined commands in the specification based on Gerber X2 generation were successfully rendered, unlike the tested commercial parsers used in the experiments. Moreover, the obtained results were comparable to those parsers regarding the commands they can execute as well as the ground-truth, emphasizing the accuracy of the proposed approach. Its top-down and recursive architecture allows easy integration with other software regardless of the platform, highlighting its potential inclusion and integration in the production of electronic circuits.

An intelligent tutoring system for supporting active learning: A case study on predictive parsing learning

The way in which people learn and institutions teach is changing due to the ever-increasing impact of technology. People access the Internet anywhere, anytime and request online training. This has brought about the creation of numerous online learning platforms which offer comprehensive and effective educational solutions which are 100% online. These platforms benefit from intelligent tutoring systems that help and guide students through the learning process, emulating the behavior of a human tutor. However, these systems give the student little freedom to experiment with the knowledge of the subject, that is, they do not allow him/her to propose and carry out tasks on his/her own initiative. They are very restricted systems in term of what the student can do, as the tasks are defined in advance.An intelligent tutoring system is proposed in this paper to encourage students to learn through experimentation, proposing tasks on their own initiative, which involves putting into use all the skills, abilities tools and knowledge needed to successfully solve them. This system has been designed developed and applied for learning predictive parsing techniques and has been used by Computer Science students during four academic courses to evaluate its suitability for improving the student’s learning process.

Purely functional GLL parsing

Abstract Generalised parsing has become increasingly important in the context of software language design and several compiler generators and language workbenches have adopted generalised parsing algorithms such as GLR and GLL. The original GLL parsing algorithms are described in low-level pseudo-code as the output of a parser generator. This paper explains GLL parsing differently, defining the FUN-GLL algorithm as a collection of pure, mathematical functions and focussing on the logic of the algorithm by omitting implementation details. In particular, the data structures are modelled by abstract sets and relations rather than specialised implementations. The description is further simplified by omitting lookahead and adopting the binary subtree representation of derivations to avoid the clerical overhead of graph construction. Conventional parser combinators inherit the drawbacks from the recursive descent algorithms they implement. Based on FUN-GLL, this paper defines generalised parser combinators that overcome these problems. The algorithm is described in the same notation and style as FUN-GLL and uses the same data structures. Both algorithms are explained as a generalisation of basic recursive descent algorithms. The generalised parser combinators of this paper have several advantages over combinator libraries that generate internal grammars. For example, with the generalised parser combinators it is possible to parse larger permutation phrases and to write parsers for languages that are not context-free. The ‘BNF combinator library’ is built around the generalised parser combinators. With the library, embedded and executable syntax specifications are written. The specifications contain semantic actions for interpreting programs and constructing syntax trees. The library takes advantage of Haskell’s type-system to type-check semantic actions and Haskell’s abstraction mechanism enables ‘reuse through abstraction’. The practicality of the library is demonstrated by running parsers obtained from the syntax descriptions of several software languages.

Ticket Collectionwith Destination Prediction in Bus Services in Urban Areas using Time Based Predictive Algorithm

In the new age of automation and machine assisted function of the human way of life people still tend to notice verification and checking of tickets in local land transport such as trains and buses to still be operated by man. This project is a proposal of a new platform and method to book these tickets of buses on a local level. This can lead to decrease in the overcrowding of buses, easy time management of commuters, and smooth functioning of the bus business. Initially the bank details of the passenger must be linked to the app.Machine learning predictive parsing algorithm in combination with data mining features enable the prediction of the passengers to and fro details on a daily and timely basis. Then a SMS alert for ticket payment proof is sent to the user. In admin side, they calculate amount details using this application. Per day amount details of specific route or bus can be calculated by accessing the database. There is also a provision where the IMEI numbers of the consumers is collected. Through GPS system the IMEI numbers of the mobiles inside the bus is checked with the IMEI numbers of those in the database. Ticket defaulters are identified if the IMEI numbers are not present in the database. The entire trail of the transit is on a non-paper sever.

Depth Embedded Recurrent Predictive Parsing Network for Video Scenes

Semantic segmentation-based scene parsing plays an important role in automatic driving and autonomous navigation. However, most of the previous models only consider static images, and fail to parse sequential images because they do not take the spatial-temporal continuity between consecutive frames in a video into account. In this paper, we propose a depth embedded recurrent predictive parsing network (RPPNet), which analyzes preceding consecutive stereo pairs for parsing result. In this way, RPPNet effectively learns the dynamic information from historical stereo pairs, so as to correctly predict the representations of the next frame. The other contribution of this paper is to systematically study the video scene parsing (VSP) task, in which we use the RPPNet to facilitate conventional image paring features by adding spatial-temporal information. The experimental results show that our proposed method RPPNet can achieve fine predictive parsing results on cityscapes and the predictive features of RPPNet can significantly improve conventional image parsing networks in VSP task.

Language history attenuates syntactic prediction in L1 processing

An eye-tracking experiment in the Visual World Paradigm was conducted to examine the effects of language history on the predictive parsing of sentences containing relative clauses in the first-learned language of fluent bilingual adults. We compared heritage speakers of Spanish (HSs)—who had spent most of their lives immersed in an English-dominant society—to Spanish–English late bilinguals (LBs), who did not begin immersion in an English-dominant society until adulthood. Consistent with studies of monolinguals, the LBs demonstrated a subject/object relative clause processing asymmetry, i.e. a processing advantage during subject relative clauses and a processing disadvantage during object relative clauses. This suggests that the LBs actively predicted the syntactic structure of subject relative clauses, consistent with the active filler hypothesis. The HSs, on the other hand, did not exhibit this processing asymmetry, suggesting less active prediction. We conclude, therefore, that decreased exposure to the first-learned language causes less active prediction in first-language processing, which causes both disadvantages, and interestingly, advantages, in processing speed.

Fake Sensor Detection and Secure Data Transmission Based on Predictive Parser in WSNs

The economic part of farming to India’s Gross Domestic Product is steady refusing with the nation broad-based economical development. Therefore, developing agriculture field is the important factor. To solve this problem we propose precision farming solution using wireless sensor network (WSN) to gain farming production in India. We use WSN as a placement of Camera Sensors in crop field that can be used to monitor the environmental condition, detect the pest, and send the pest details to the corresponding former. The use of pesticides in crop growing is necessary to assert the quality of large-scale product. However, the attacker inserts the fake sensor for destruct the crop field from pests. In WSN, the sensors are equipped with irreplaceable batteries and qualified by limited computing ability. Thus, reducing the sensor energy depletion is an important factor. To overcome these problems we propose fake sensor detection and secure data transmission based on predictive parser in WSNs (FSD-PP). The main goal is the operation of a WSN for early snail pest detection to diminish the use of pesticides in crops and gain more productivity in the cultivation. Here, the predictive parser method is used to check the sensor authentication. Elliptical curve cryptography algorithm is used to remove the eaves dropping attack in the network. The sleep/awake scheduling algorithm is used to save the sensor energy. The simulation result demonstrates that the proposed scheme will enhance the network life span and secure data transmission from sensor to base station in WSN.

Predictive sentence comprehension during story-listening in autism spectrum disorder

ABSTRACTIndividuals with Autism Spectrum Disorder (ASD) show a range of language production deficits, however, language comprehension in ASD remains under-studied in part because co-morbid social deficits affect behavioural compliance. This challenge can be overcome by engaging participants in a naturalistic task while passively collecting neural signals. To test predictive processing with naturalistic language, we collect MEG data while 16 8–12-year-old high-functioning participants with a clinical diagnosis of ASD and 16 age- and gender-matched typically developing peers listen to an audiobook story. The neuromagnetic signals are correlated with word-by-word states from a computational model that quantifies incremental sentence predictions in terms of surprisal. Consistent with prior eye-tracking work, our results are compatible with predictive parsing that is equivalent between high-functioning individuals with ASD and TD peers.

The temporal dynamics of structure and content in sentence comprehension: Evidence from fMRI-constrained MEG.

Humans have a striking capacity to combine words into sentences that express new meanings. Previous research has identified key brain regions involved in this capacity, but little is known about the time course of activity in these regions, as hemodynamic methods such as fMRI provide little insight into temporal dynamics of neural activation. We performed an MEG experiment to elucidate the temporal dynamics of structure and content processing within four brain regions implicated by fMRI data from the same experiment: the temporo-parietal junction (TPJ), the posterior temporal lobe (PTL), the anterior temporal lobe (ATL), and the anterior inferior frontal gyrus (IFG). The TPJ showed increased activity for both structure and content near the end of the sentence, consistent with a role in incremental interpretation of event semantics. The PTL, a region not often associated with core aspects of syntax, showed a strong early effect of structure, consistent with predictive parsing models, and both structural and semantic context effects on function words. These results provide converging evidence that the PTL plays an important role in lexicalized syntactic processing. The ATL and IFG, regions traditionally associated with syntax, showed minimal effects of sentence structure. The ATL, PTL and IFG all showed effects of semantic content: increased activation for real words relative to nonwords. Our fMRI-guided MEG investigation therefore helps identify syntactic and semantic aspects of sentence comprehension in the brain in both spatial and temporal dimensions.

GLL syntax analysers for EBNF grammars

GLL is a worst-case cubic, recursive descent based parsing technique which can be applied to all BNF grammars without the need for grammar modification. However, EBNF grammars are often used, both for their compactness and their relative expressive simplicity. In this paper we give a formal specification for a parse tree representation of derivations which reflects the EBNF structure of the grammar, is worst case cubic size, and captures all derivations in the case of ambiguity. Particular care is need in the case of closures with nullable bodies. We also describe an extension of GLL which directly supports the EBNF constructs. The resulting parsers are worst case cubic and follow the structure of the specifying EBNF grammar, making the parser behaviour easy to reason about. The parsers exploit the efficiency of factorisation and the use of iteration rather than recursion, retaining the structure of the specification in the presence of embedded semantic actions. • An SPPF representation of all derivations in any EBNF grammar is developed. • The SPPFs are compact, worst case cubic size, and fully general. • A worst case cubic general EBNF GLL parser that constructs these SPPFs is presented. • Practicality Is demonstrated using the Java standard grammars.

PAVT: a tool to visualize and teach parsing algorithms

A parsing algorithm visualizer is a tool that visualizes the construction of a parser for a given context-free grammar and then illustrates the use of that parser to parse a given string. Parsing algorithm visualizers are used to teach the course on compiler construction which in invariably included in all undergraduate computer science curricula. This paper presents a new parsing algorithm visualizer that can visualize six parsing algorithms, viz. predictive parsing, simple LR parsing, canonical LR parsing, look-ahead LR parsing, Earley parsing and CYK parsing. The tool logically explains the process of parsing showing the calculations involved in each step. The output of the tool has been structured to maximize the learning outcomes and contains important constructs like FIRST and FOLLOW sets, item sets, parsing table, parse tree and leftmost or rightmost derivation depending on the algorithm being visualized. The tool has been used to teach the course on compiler construction at both undergraduate and graduate levels. An overall positive feedback was received from the students with 89% of them saying that the tool helped them in understanding the parsing algorithms. The tool is capable of visualizing multiple parsing algorithms and 88% students used it to compare the algorithms.

Derivatives of Parsing Expression Grammars

This paper introduces a new derivative parsing algorithm for recognition of parsing expression grammars. Derivative parsing is shown to have a polynomial worst-case time bound, an improvement on the exponential bound of the recursive descent algorithm. This work also introduces asymptotic analysis based on inputs with a constant bound on both grammar nesting depth and number of backtracking choices; derivative and recursive descent parsing are shown to run in linear time and constant space on this useful class of inputs, with both the theoretical bounds and the reasonability of the input class validated empirically. This common-case constant memory usage of derivative parsing is an improvement on the linear space required by the packrat algorithm.

PHRASE STRUCTURE BASED ENGLISH TO KANNADA SENTENCE TRANSLATION

In order to build a natural language processing system first the words are placed into a structured form that leads to a syntactically correct sentence. Syntactic analysis of a sentence is performed by parsing technique. This paper explores the novel approach that how the shift reduce parsing technique is used for translating English sentences into a grammatically correct Kannada sentences by reordering of English parse tree structure, generating and implementing phrase structure grammar(PSG) for kannada sentences. Recursive Descent Parsing technique is used to generate English phrase tree structure and terminal symbols are tagged with Kannada equivalent words then Shift-Reduce Parsing technique is used to construct a Kannada sentence. Part-of-Speech (POS) tagger is used to tag Kannada words to English words. It is implemented by using supervised machine learning approach

Parsing Expression Grammars with Unordered Choices

Parsing expression grammars (PEGs) were formalized by Ford in 2004, and have several pragmatic operators (such as ordered choice and unlimited lookahead) for better expressing modern programming language syntax. In addition, PEGs can be parsed in a linear time by using recursive-descent parsing and memoization. In this way, PEGs have a lot of positive aspects. On the other hand, it is known that ordered choices defy intuition. They may cause bugs. This is due to a priority of an ordered choice. To avoid this, unordered choices are required. In this paper, we define a parsing expression grammar with unordered choices (PEGwUC), an extension of a PEG with unordered choices. By the extension, it is expected that a PEGwUC includes both a PEG and a context-free grammar (CFG), and this allows us to write a grammar more intuitively. Furthermore, we show an algorithm for parsing a PEGwUC. The algorithm runs in a linear time when a PEGwUC does not include unordered choice and in a cubic time in worst-case running time.

Cut Points in PEG

Parsing Expression Grammar (PEG) encodes a recursive-descent parser with limited backtracking. It has been recently noticed that in the situation when the parser is to explore several alternatives one after another, no further alternatives need to be explored after the parser reached certain “cut p oint”. This fact can be used to save both processing time and storage. The subject of the paper is identification of cut points, which can also help in producing better diagnostics.

Arpeggio: A flexible PEG parser for Python

Arpeggio is a recursive descent parser with full backtracking and memoization based on PEG (Parsing Expression Grammar) grammars. This category of parsers is known as packrat parsers. It is implemented in the Python programming language and works as a grammar interpreter.Arpeggio has a very good support for error reporting, debugging, and grammar and parse tree visualization. It is used in industrial environments and teaching Domain-Specific Languages course at the Faculty of Technical Sciences in Novi Sad. Arpeggio is a foundation of a high-level DSL meta-language and tool - textX.It is a free and open-source software available at GitHub under MIT license.