Strong Normalization by Type-Directed Partial Evaluation and Run-Time Code Generation (Preliminary Version)

We investigate the synergy between type-directed partial evaluation and run-time code generation for the Caml dialect of ML. Type-directed partial evaluation maps simply typed, closed Caml values to a representation of their long beta-eta-normal form. Caml uses a virtual machine and has the capability to load byte code at run time. Representing the long beta-eta-normal forms as byte code gives us the ability to strongly normalize higher-order values (i.e., weak head normal forms in ML), to compile the resulting strong normal forms into byte code, and to load this byte code all in one go, at run time.We conclude this note with a preview of our current work on scalingup strong normalization by run-time code generation to the Camlmodule language

An Analytical Approach to Programs as Data Objects

This essay accompanies a selection of 32 articles (referred to in bold face in the text and marginally marked in the bibliographic references) submitted to Aarhus University towards a Doctor Scientiarum degree in Computer Science.The author's previous academic degree, beyond a doctoral degree in June 1986, is an "Habilitation à diriger les recherches" from the Université Pierre et Marie Curie (Paris VI) in France; the corresponding material was submitted in September 1992 and the degree was obtained in January 1993.The present 32 articles have all been written since 1993 and while at DAIMI.Except for one other PhD student, all co-authors are or have been the author's students here in Aarhus

Type-Directed Weaving of Aspects for Higher-order Functional Languages

Aspect-oriented programming (AOP) has been shown to be a useful model for software development. Special care must be taken when we try to adapt AOP to strongly typed functional languages which come with features like a type inference mechanism, polymorphic types, higher-order functions and type-scoped pointcuts. Our main contribution lies in a seamless integration of these two paradigms through a static weaving process which deals with around advices with type-scoped pointcuts in the presence of higher-order functions. We give a source-level type inference system for a higher-order, polymorphic language coupled with type-scoped pointcuts. The type system ensures that base programs are oblivious to the type of around advices. We present a type-directed translation scheme which resolves all advice applications at static time. The translation removes advice declarations from source programs and produces translated code which is typable in the Hindley-Milner system

Normalization by Evaluation with Typed Abstract Syntax

We present a simple way to implement typed abstract syntax for thelambda calculus in Haskell, using phantom types, and we specify normalization by evaluation (i.e., type-directed partial evaluation) to yield thistyped abstract syntax. Proving that normalization by evaluation preserves types and yields normal forms then reduces to type-checking thespecification

A Simple Solution to Type Specialization

Partial evaluation specializes terms, but traditionally thisspecialization does not apply to the type of these terms. As a result, specializing, e.g., an interpreter written in a typed language, which requires a "universal" type to encode expressible values, yields residual programs with type tags all over. Neil Jones has stated that getting rid of these type tags was an open problem, despite possible solutions such as Torben Mogensen's "constructor specialization." To solve this problem, John Hughes has proposed a new paradigm for partial evaluation, "Type Specialization," based on type inference instead of being based on symbolic interpretation. Type Specialization is very elegant in principle but it also appears non-trivial in practice. Stating the problem in terms of types instead of in terms of type encodings suggests a very simple type-directed solution, namely, to use a projection from the universal type to the specific type of the residual program. Standard partial evaluation then yields a residual programwithout type tags, simply and efficiently

A Symmetric Approach to Compilation and Decompilation

Just as specializing a source interpreter can achieve compilation from a source language to a target language, we observe that specializing a target interpreter can achieve compilation from the target language to the source language. In both cases, the key issue is the choice of whether to perform an evaluation or to emit code that represents this evaluation. We substantiate this observation by specializing two source interpreters and two target interpreters. We first consider a source language of arithmetic expressions and a target language for a stack machine, and then the lambda-calculus and the SECD-machine language. In each case, we prove that the target-to-source compiler is a left inverse of the source-to-target compiler, i.e., it is a decompiler. In the context of partial evaluation, compilation by source-interpreter specialization is classically referred to as a Futamura projection. By symmetry, it seems logical to refer to decompilation by target-interpreter specialization as a Futamura embedding

Meta-F*: Proof Automation with SMT, Tactics, and Metaprograms

We introduce Meta-F*, a tactics and metaprogramming framework for the F* program verifier. The main novelty of Meta-F* is allowing the use of tactics and metaprogramming to discharge assertions not solvable by SMT, or to just simplify them into well-behaved SMT fragments. Plus, Meta-F* can be used to generate verified code automatically. Meta-F* is implemented as an F* effect, which, given the powerful effect system of F*, heavily increases code reuse and even enables the lightweight verification of metaprograms. Metaprograms can be either interpreted, or compiled to efficient native code that can be dynamically loaded into the F* type-checker and can interoperate with interpreted code. Evaluation on realistic case studies shows that Meta-F* provides substantial gains in proof development, efficiency, and robustness.Comment: Full version of ESOP'19 pape

Time-Efficient Hybrid Approach for Facial Expression Recognition

Facial expression recognition is an emerging research area for improving human and computer interaction. This research plays a significant role in the field of social communication, commercial enterprise, law enforcement, and other computer interactions. In this paper, we propose a time-efficient hybrid design for facial expression recognition, combining image pre-processing steps and different Convolutional Neural Network (CNN) structures providing better accuracy and greatly improved training time. We are predicting seven basic emotions of human faces: sadness, happiness, disgust, anger, fear, surprise and neutral. The model performs well regarding challenging facial expression recognition where the emotion expressed could be one of several due to their quite similar facial characteristics such as anger, disgust, and sadness. The experiment to test the model was conducted across multiple databases and different facial orientations, and to the best of our knowledge, the model provided an accuracy of about 89.58% for KDEF dataset, 100% accuracy for JAFFE dataset and 71.975% accuracy for combined (KDEF + JAFFE + SFEW) dataset across these different scenarios. Performance evaluation was done by cross-validation techniques to avoid bias towards a specific set of images from a database