Mahmuda Naznin

Jaid Monwar Chowdhury, Ahmad Farhan Shahriar Chowdhury, Humayra Binte Monwar et al.

Multi-view code graphs that align abstract syntax trees, control flow graphs, and data flow graphs are now central to machine-learning models for software engineering. For C and C++, no single tool produces these aligned views without a complete build. We present ATLAS, a command-line tool that takes one or more C or C++ source files and emits an AST, a source-level inter-procedural CFG, a reaching-definition DFG, or any combination. The output is available as JSON, DOT, or PNG. ATLAS runs directly on source code and accepts partial input such as a project with missing headers, so it needs no compilation or build database. All views share one node namespace, so a downstream consumer can recover any single view by filtering edges. Command-line flags select views, collapse variables, and blacklist node categories to resize the emitted graph. On the TheAlgorithms corpora, ATLAS produces a correct CFG for 96.80% of C files and 91.67% of C++ files, including files that do not compile. It already serves as the CFG front-end of an LLM-based unit-test generation framework for C. ATLAS is open source and ships as a Docker image with a screencast walkthrough. Demo link: https://youtu.be/50DvEbenp14.

Sharmin Akther Purabi, Rayhan Rashed, Md. Mirajul Islam et al.

Identifying physical traits and emotions based on system-sensed physical activities is a challenging problem in the realm of human-computer interaction. Our work contributes in this context by investigating an underlying connection between head movements and corresponding traits and emotions. To do so, we utilize a head movement measuring device called eSense, which gives acceleration and rotation of a head. Here, first, we conduct a thorough study over head movement data collected from 46 persons using eSense while inducing five different emotional states over them in isolation. Our analysis reveals several new head movement based findings, which in turn, leads us to a novel unified solution for identifying different human traits and emotions through exploiting machine learning techniques over head movement data. Our analysis confirms that the proposed solution can result in high accuracy over the collected data. Accordingly, we develop an integrated unified solution for real-time emotion and trait identification using head movement data leveraging outcomes of our analysis.