Ayesha Sadiq

Asad Mansoor Khan, Ayesha Sadiq, Sajid Gul Khawaja et al.

Daily life of thousands of individuals around the globe suffers due to physical or mental disability related to limb movement. The quality of life for such individuals can be made better by use of assistive applications and systems. In such scenario, mapping of physical actions from movement to a computer aided application can lead the way for solution. Surface Electromyography (sEMG) presents a non-invasive mechanism through which we can translate the physical movement to signals for classification and use in applications. In this paper, we propose a machine learning based framework for classification of 4 physical actions. The framework looks into the various features from different modalities which contribution from time domain, frequency domain, higher order statistics and inter channel statistics. Next, we conducted a comparative analysis of k-NN, SVM and ELM classifier using the feature set. Effect of different combinations of feature set has also been recorded. Finally, the classifier accuracy with SVM and 1-NN based classifier for a subset of features gives an accuracy of 95.21 and 95.83 respectively. Additionally, we have also proposed that dimensionality reduction by use of PCA leads to only a minor drop of less than 5.55% in accuracy while using only 9.22% of the original feature set. These finding are useful for algorithm designer to choose the best approach keeping in mind the resources available for execution of algorithm.

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.