I am a computational biologist interested in interpretable machine learning for genomics and health care. Interpretable ...

The 1990s were a boom time for new programming languages. The rise of the internet and the increasing complexity of software ...

As a technical discipline, the field of AI began in the mid-20th century. In 1950, British mathematician Alan Turing proposed ...

For example, researchers have introduced adaptive learning activities that incorporate the Revised Bloom Taxonomy (RBT) to align with students’ cognitive skills in programming education (Troussas et ...

Xaba is on a mission to change automation from simple mechanization to a connected, intelligent ecosystem, enabling factory machines to self-program, self-optimize, and run without a single line of co ...

Other studies have already used machine learning in related ways. For example, some researchers built models to classify physical activity in children using motion sensors.

Scheduling and dispatching tools for high-performance computing (HPC) machines have the key role of mapping jobs to the available resources, trying to maximize performance and quality-of-service (QoS) ...

javascript bytecode virtual-machine static-analysis reverse-engineering deobfuscation educational research-tool opcodes anti-bot stack-machine disassembly security-research tiktok devirtualization ...

This repository contains solutions to a variety of algorithmic and data structure problems from popular platforms such as LeetCode, HackerRank, Codility, and WorkAtTech. The problems covered in this ...

For programming, you'll probably want to stick to GPT-4o, because that model aced all our tests. But it might be interesting to cross-check your code across the different LLMs.