Advances in the Design and Implementation of a Multi-Tier Architecture in the GIPSY Environment

We present advances in the software engineering design and implementation of the multi-tier run-time system for the General Intensional Programming System (GIPSY) by further unifying the distributed technologies used to implement the Demand Migration Framework (DMF) in order to streamline distributed execution of hybrid intensional-imperative programs using Java.Comment: 11 pages, 3 figure

Towards a multi-tier runtime system for GIPSY

Intensional programming implies declarative programming, in the sense of Lucid, based on denotational semantics where the declarations are evaluated in an inherent multi-dimensional context space. The General Intensional Programming System (GIPSY) is a hybrid multi-language programming platform and a demand-driven execution environment. GIPSY aims at the long-term investigation into the possibilities of Intensional Programming. The GIPSY's compiler, GIPC, is based on the notion of Generic Intensional Programming Language (GIPL) which solved the problem of language-independence of the runtime system by allowing a common representation for all compiled programs, the Generic Eduction Engine Resources (GEER). In this thesis, we discuss the solution to GIPSY's Runtime System. The Multi-Tier framework which consists of Demand Generator Tier (DGT), Demand Store Tier (DST) and Demand Worker Tier (DWT), offers demand-driven, distributed execution and technology independent manners by integrating the previous research on the demand migration middle-ware implemented by Jini and Java Message Service (JMS

A GIPSY Runtime System with a Kubernetes Underlay for the OpenTDIP Forensic Computing Backend

In this research work, we propose an underlay based on Kubernetes to enhance the scalable fault tolerance of the General Intensional Programming System's distributed run-time demand-driven backend to gather digital evidence from GitHub repositories and encode them in Forensic Lucid for further analysis in the integrated OpenTDIP environment. We developed a solution so that forensic investigators could use GitHub to gather a dataset to investigate program flaws and vulnerabilities related to security from GitHub projects written in different programming languages. For this purpose, we design and implement a JSON demand-driven encoder to perform a Forensic Lucid conversion pipeline (data extraction, format conversion, and file compilation). In order to distribute the execution, we utilized the GIPSY distributed computing system. We also integrated Kubernetes with GIPSY distributed computing system in order to improve the configuring, starting up and registering GIPSY nodes, so that GIPSY nodes could get registered automatically without any manual configuration. In addition, provide a mechanism to have a scalable fault-tolerant system so that when a GIPSY node dies, it will handle reallocation, configuration and registration of the GIPSY nodes automatically

MARFL: An Intensional Language for Demand-Driven Management of Machine Learning Backends

Artificial Intelligence (AI) is a rapidly evolving field that has transformed numerous industries and one of its key applications, Pattern Recognition, has been instrumental to the success of Large Language Models like ChatGPT, Bard, etc. However, scripting these advanced systems can be complex and challenging for some users. In this research, we propose a simpler scripting language to perform complex pattern recognition tasks. We introduce a new intensional programming language, MARFL, which is an extension of the Lucid family supported by General Intensional Programming System (GIPSY). Our solution focuses on providing syntax and semantics for MARFL, which enables scripting of Modular A* Recognition Framework (MARF)-based applications as context aware, where the notion of context represents fine-grained configuration details of a given MARF instance. We adapt the concept of context to provide an easily comprehensible language that can perform complex pattern recognition tasks on a demand-driven system such as GIPSY. Our solution is also generic enough to handle other machine learning backends such as PyTorch or TensorFlow in the future. We also provide a complete implementation of our approach, including a new compiler component and MARFL-specific execution engines within GIPSY. Our work extends the use of intensional programming to modeling and executing scripted pattern recognition tasks, which can be used for implementing different algorithmic specifications. Additionally, we utilize the demand-driven distributed computing capabilities of GIPSY to enable an efficient and scalable execution