scholarly journals FreeST: Context-free Session Types in a Functional Language

2019 ◽  
Vol 291 ◽  
pp. 12-23
Author(s):  
Bernardo Almeida ◽  
Andreia Mordido ◽  
Vasco T. Vasconcelos
Keyword(s):  
Functional Language ◽  
Session Types ◽  
Context Free

scholarly journals Propositions as sessions

2014 ◽  
Vol 24 (2-3) ◽  
pp. 384-418 ◽  
Author(s):  
PHILIP WADLER
Keyword(s):  
Linear Functional ◽  
Linear Logic ◽  
Functional Language ◽  
Session Types ◽  
Deadlock Freedom ◽  
First Time ◽  
Standard Presentation

AbstractContinuing a line of work by Abramsky (1994), Bellin and Scott (1994), and Caires and Pfenning (2010), among others, this paper presents CP, a calculus, in which propositions of classical linear logic correspond to session types. Continuing a line of work by Honda (1993), Hondaet al. (1998), and Gay & Vasconcelos (2010), among others, this paper presents GV, a linear functional language with session types, and a translation from GV into CP. The translation formalises for the first time a connection between a standard presentation of session types and linear logic, and shows how a modification to the standard presentation yields a language free from races and deadlock, where race and deadlock freedom follows from the correspondence to linear logic.

Translating imperative algorithms into declarative, functional terms

2017 ◽  
Author(s):  
C. M. Sperberg-McQueen
Keyword(s):  
Data Structure ◽  
Data Structures ◽  
Input String ◽  
General Context ◽  
Functional Language ◽  
Context Free Grammar ◽  
Defining Relations ◽  
Small Set ◽  
Procedural Description ◽  
Context Free

In building up subroutine libraries for XSLT and XQuery, it is sometimes useful to re-implement standard algorithms in the new language. Such re-implementation can be challenging, because standard algorithms are often described in imperative terms; before being reimplemented in XSLT or XQuery, the algorithm must first be re-understood in a declarative and functional way. Some of the challenges which arise in this process can be illustrated by the example of Earley parsing. Earley’s algorithm can parse an input string against any context-free grammar in Backus-Naur Form. Unlike recursive-descent or table-driven LALR(1) parsers it is not limited to “well-behaved” grammars. Unlike other general context-free parsing algorithms such as CYK, it does not devote time and space to operations which can be seen in advance to have no possible use in a full parse. Earley’s procedural description involves successive changes to a small set of data structures representing sets of Earley items; these procedural changes cannot be translated directly into a functional language lacking assignment. But Earley’s data-structure updates can be understood as defining relations among Earley items, and the algorithm as a whole can be interpreted as calculating the smallest set of Earley items which contains a given starter item and is closed over a small number of relations on items. Re-thinking the Earley algorithm in this way not only makes it easier to implement it in XSLT and XQuery, but helps make it clear why the parser is both complete (it will always find a parse if there is one) and correct (any parse it finds will be a real parse).

scholarly journals Context-Free Session Types for Applied Pi-Calculus

2018 ◽  
Vol 276 ◽  
pp. 3-18
Author(s):  
Jens Aagaard ◽  
Hans Hüttel ◽  
Mathias Jakobsen ◽  
Mikkel Kettunen
Keyword(s):  
Session Types ◽  
Pi Calculus ◽  
Context Free

scholarly journals Type checking a multithreaded functional language with session types

2006 ◽  
Vol 368 (1-2) ◽  
pp. 64-87 ◽  
Author(s):  
Vasco T. Vasconcelos ◽  
Simon J. Gay ◽  
António Ravara
Keyword(s):  
Functional Language ◽  
Type Checking ◽  
Session Types

scholarly journals Deciding the Bisimilarity of Context-Free Session Types

2020 ◽  
pp. 39-56
Author(s):  
Bernardo Almeida ◽  
Andreia Mordido ◽  
Vasco T. Vasconcelos
Keyword(s):  
Session Types ◽  
Soundness And Completeness ◽  
Context Free

Abstract We present an algorithm to decide the equivalence of context-free session types, practical to the point of being incorporated in a compiler. We prove its soundness and completeness. We further evaluate its behaviour in practice. In the process, we introduce an algorithm to decide the bisimilarity of simple grammars.

Context-free session types

2016 ◽  
Vol 51 (9) ◽  
pp. 462-475 ◽  
Author(s):  
Peter Thiemann ◽  
Vasco T. Vasconcelos
Keyword(s):  
Session Types ◽  
Context Free

Assessing Functional Language in School-Aged Children Using Language Sample Analysis

2020 ◽  
Vol 5 (3) ◽  
pp. 622-636
Author(s):  
John Heilmann ◽  
Alexander Tucci ◽  
Elena Plante ◽  
Jon F. Miller
Keyword(s):  
Computer Hardware ◽  
School Age Children ◽  
Functional Language ◽  
School Age ◽  
Clinical Use ◽  
Sample Analysis ◽  
Validity And Reliability ◽  
Language Sample Analysis ◽  
Accuracy And Repeatability ◽  
Language Sample

Purpose The goal of this clinical focus article is to illustrate how speech-language pathologists can document the functional language of school-age children using language sample analysis (LSA). Advances in computer hardware and software are detailed making LSA more accessible for clinical use. Method This clinical focus article illustrates how documenting school-age student's communicative functioning is central to comprehensive assessment and how using LSA can meet multiple needs within this assessment. LSA can document students' meaningful participation in their daily life through assessment of their language used during everyday tasks. The many advances in computerized LSA are detailed with a primary focus on the Systematic Analysis of Language Transcripts (Miller & Iglesias, 2019). The LSA process is reviewed detailing the steps necessary for computers to calculate word, morpheme, utterance, and discourse features of functional language. Conclusion These advances in computer technology and software development have made LSA clinically feasible through standardized elicitation and transcription methods that improve accuracy and repeatability. In addition to improved accuracy, validity, and reliability of LSA, databases of typical speakers to document status and automated report writing more than justify the time required. Software now provides many innovations that make LSA simpler and more accessible for clinical use. Supplemental Material https://doi.org/10.23641/asha.12456719

Framework for rare event detection using Artificial Neural Network based context free grammar

2020 ◽  
Vol 39 (6) ◽  
pp. 8463-8475
Author(s):  
Palanivel Srinivasan ◽  
Manivannan Doraipandian
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Event Detection ◽  
Performance Metrics ◽  
Rare Events ◽  
Rare Event ◽  
Video Stream ◽  
Context Free Grammar ◽  
Artificial Neural ◽  
Context Free

Rare event detections are performed using spatial domain and frequency domain-based procedures. Omnipresent surveillance camera footages are increasing exponentially due course the time. Monitoring all the events manually is an insignificant and more time-consuming process. Therefore, an automated rare event detection contrivance is required to make this process manageable. In this work, a Context-Free Grammar (CFG) is developed for detecting rare events from a video stream and Artificial Neural Network (ANN) is used to train CFG. A set of dedicated algorithms are used to perform frame split process, edge detection, background subtraction and convert the processed data into CFG. The developed CFG is converted into nodes and edges to form a graph. The graph is given to the input layer of an ANN to classify normal and rare event classes. Graph derived from CFG using input video stream is used to train ANN Further the performance of developed Artificial Neural Network Based Context-Free Grammar – Rare Event Detection (ACFG-RED) is compared with other existing techniques and performance metrics such as accuracy, precision, sensitivity, recall, average processing time and average processing power are used for performance estimation and analyzed. Better performance metrics values have been observed for the ANN-CFG model compared with other techniques. The developed model will provide a better solution in detecting rare events using video streams.

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