In and Out of SSA : a Denotational Specification

International audienceWe present non-standard denotational specifications of the SSA form and of its conversion processes from and to imperative programming languages. Thus, we provide a strong mathematical foundation for this intermediate code representation language used in modern compilers such as GCC or Intel CC. More specifically, we provide (1) a new functional approach to SSA, the Static Single Assignment form, together with its denotational semantics, (2) a collecting denotational semantics for a simple imperative language Imp, (3) a non-standard denotational semantics specifying the conversion of Imp to SSA and (4) a non-standard denotational semantics for the reverse SSA to Imp conversion process. These translations are proven correct, ensuring that the structure of the memory states manipulated by imperative constructs is preserved in compilers' middle ends that use the SSA form as control-flow data representation. Interestingly, a s unexpected by-products of our conversion procedures, we offer (1) a new proof of the reducibility of the RAM computing model to the domain of Kleene's partial recursive functions, to which SSA is strongly related, and, on a more practical note, (2) a new algorithm to perform program slicing in imperative programming languages. All these specifications have been prototyped using GNU Common Lisp. These fundamental results prove that the widely used SSA technology is sound. Our formal denotational framework further suggests that the SSA form could become a target of choice for other optimization analysis techniques such as abstract interpretation or partial evaluation. Indeed, since the SSA form is language-independent, the resulting optimizations would be automatically enabled for any source language supported by compilers such as GCC

Toward an Energy Efficient Language and Compiler for (Partially) Reversible Algorithms

We introduce a new programming language for expressing reversibility, Energy-Efficient Language (Eel), geared toward algorithm design and implementation. Eel is the first language to take advantage of a partially reversible computation model, where programs can be composed of both reversible and irreversible operations. In this model, irreversible operations cost energy for every bit of information created or destroyed. To handle programs of varying degrees of reversibility, Eel supports a log stack to automatically trade energy costs for space costs, and introduces many powerful control logic operators including protected conditional, general conditional, protected loops, and general loops. In this paper, we present the design and compiler for the three language levels of Eel along with an interpreter to simulate and annotate incurred energy costs of a program.Comment: 17 pages, 0 additional figures, pre-print to be published in The 8th Conference on Reversible Computing (RC2016

Ontology-based Fuzzy Markup Language Agent for Student and Robot Co-Learning

An intelligent robot agent based on domain ontology, machine learning mechanism, and Fuzzy Markup Language (FML) for students and robot co-learning is presented in this paper. The machine-human co-learning model is established to help various students learn the mathematical concepts based on their learning ability and performance. Meanwhile, the robot acts as a teacher's assistant to co-learn with children in the class. The FML-based knowledge base and rule base are embedded in the robot so that the teachers can get feedback from the robot on whether students make progress or not. Next, we inferred students' learning performance based on learning content's difficulty and students' ability, concentration level, as well as teamwork sprit in the class. Experimental results show that learning with the robot is helpful for disadvantaged and below-basic children. Moreover, the accuracy of the intelligent FML-based agent for student learning is increased after machine learning mechanism.Comment: This paper is submitted to IEEE WCCI 2018 Conference for revie

Treebank-based acquisition of LFG resources for Chinese

This paper presents a method to automatically acquire wide-coverage, robust, probabilistic Lexical-Functional Grammar resources for Chinese from the Penn Chinese Treebank (CTB). Our starting point is the earlier, proofof- concept work of (Burke et al., 2004) on automatic f-structure annotation, LFG grammar acquisition and parsing for Chinese using the CTB version 2 (CTB2). We substantially extend and improve on this earlier research as regards coverage, robustness, quality and fine-grainedness of the resulting LFG resources. We achieve this through (i) improved LFG analyses for a number of core Chinese phenomena; (ii) a new automatic f-structure annotation architecture which involves an intermediate dependency representation; (iii) scaling the approach from 4.1K trees in CTB2 to 18.8K trees in CTB version 5.1 (CTB5.1) and (iv) developing a novel treebank-based approach to recovering non-local dependencies (NLDs) for Chinese parser output. Against a new 200-sentence good standard of manually constructed f-structures, the method achieves 96.00% f-score for f-structures automatically generated for the original CTB trees and 80.01%for NLD-recovered f-structures generated for the trees output by Bikel’s parser