Ijaz Ahmed

Qasim Bin Saeed, Ijaz Ahmed

The increasing prevalence of mental health disorders, such as depression, anxiety, and bipolar disorder, calls for immediate need in developing tools for early detection and intervention. Social media platforms, like Reddit, represent a rich source of user-generated content, reflecting emotional and behavioral patterns. In this work, we propose a multi-modal deep learning framework that integrates linguistic and temporal features for early detection of mental health crises. Our approach is based on the method that utilizes a BiLSTM network both for text and temporal feature analysis, modeling sequential dependencies in a different manner, capturing contextual patterns quite well. This work includes a cross-modal attention approach that allows fusion of such outputs into context-aware classification of mental health conditions. The model was then trained and evaluated on a dataset of labeled Reddit posts preprocessed using text preprocessing, scaling of temporal features, and encoding of labels. Experimental results indicate that the proposed architecture performs better compared to traditional models with a validation accuracy of 74.55% and F1-Score of 0.7376. This study presents the importance of multi-modal learning for mental health detection and provides a baseline for further improvements by using more advanced attention mechanisms and other data modalities.

Ayesha Sadiq, Yuan-Fang Li, Li Li et al.

In mainstream programming languages such as Java, a common way to enable concurrency is to manually introduce explicit concurrency constructs such as multi-threading. In multi-threaded programs, managing synchronization between threads is a complicated and challenging task for the programmers due to thread interleaving and heap interference that leads to problems such as deadlocks, data races. With these considerations in mind, access permission-based dependencies have been investigated as an alternative approach to verify the correctness of multi-threaded programs and to exploit the implicit concurrency present in sequential programs without using explicit concurrency constraints. However, significant annotation overhead can arise from manually adding permission-based specifications in a source program, diminishing the effectiveness of existing permission-based approaches. In this paper,we present a framework, Sip4J, to automatically extract access permission-based implicit dependencies from sequential Java programs, by performing inter-procedural static analysis of the source code. Moreover, we integrate and extend an existing permission-based verification tool, Pulse, to automatically verify correctness of the inferred specifications and to reason about their concurrent behaviors. Our evaluation on some widely-used benchmarks gives strong evidence of the correctness of the inferred annotations and their effectiveness in enabling concurrency in sequential programs.