Asif Zubayer Palak

In recent years, pattern recognition has advanced, allowing computers to detect and classify things from various sources. Recent advances in image and pattern recognition research can enhance user experience for various applications. Our research aims to address a common issue for shoppers: locating apparel with a specific style or pattern. Our system uses Computer Vision and Pattern Recognition to recognise designs on clothing based on user sketches and finds the best images or products online. To train our model, we will collect doodle sketches from end users by presenting them with a clothing product image. The model will extract features based on shape, colour, and design. The model will then generate a search query which will be inputted on a search engine to find the product that most closely resembles the doodle. Our methodology enables shoppers to find desired clothing without excessive browsing, improving human-computer connection and simplifying purchasing experiences.

Code translation from one programming language to another has been a topic of interest for academia and industry for a long time, and has recently re-emerged with the advent of Large Language Models (LLMs). While progress has been made in translating small code snippets, tackling larger projects with intricate dependencies remains a challenging task. A significant challenge in automating such translations is validating the resulting code. Translating existing tests to the target language can introduce errors, yielding potentially misleading quality assurance even when all the translated tests pass. We propose the idea of testing the translated code using the existing, untranslated tests written in the original programming language. The key to our idea is to leverage language interoperability to run code written in two different languages together. This partial translation approach offers two main benefits: (1) the ability to leverage original tests for validating translated code, not only from the project being translated but also from the clients using this project, and (2) the continuous maintainability and testability of the project during translation. We evaluate our approach by translating from Java to Python two popular Java libraries, Apache Commons CLI and Apache Commons CSV, with 1209 lines of code (in 22 Java files) and 860 lines of code (in 10 Java files), respectively. Our implementation uses Oracle’s GraalVM framework for language interoperability. We successfully validate the translation using the original Java tests, not just from the CLI and CSV libraries themselves but also from client projects of these libraries (30 for CLI and 6 for CSV). Our approach is the first to systematically and semi-automatically validate translations for such nontrivial libraries.