Gulsher Laghari

Human Activity Recognition has become inevitable in wearable technology in healthcare and fitness tracking applications. Precise recognition of daily routine human activities using wearable sensors remains a challenging task due to sensor modality, placement, and environmental factors. Indeed, Machine Learning and Deep Learning models have become robust in human activity recognition, yet these face numerous challenges in precisely recognizing the daily human activities. One major challenge is the variability in sensors readings where the same activity may lead to different sensor readings, intra-class variability, as different people perform the same activity differently or use different devices. To address this, we propose a multi-head transformer model with a multi-head attention mechanism that explicitly handles the intra-class variability. We demonstrate that the multi-head Transformer model exhibits enhanced robustness and performs better even in the face of such variability, although variability is a major problem in the popular PAMAP2 dataset, since it directly impacts the performance of deep learning models.

Spectrum-based fault localization (SBFL) is an automated fault localization technique that uses risk evaluation metrics to compute the suspiciousness scores from program spectra. Thus, risk evaluation metrics determine the technique’s performance. However, the existing experimental studies still show no optimal metric for different program structures and error types. It is possible to further optimize SBFL’s performance by combining different metrics. Therefore, this paper effectively explores the combination of risk evaluation metrics for precise fault localization. Based on extensive experiments using 92 faults from SIR and 357 faults from Defects4J repositories, we highlight what and which risk evaluation metrics to combine to maximize the efficiency and accuracy of fault localization. The experimental results show that combining risk evaluation metrics with high negative correlation values can improve fault localization effectiveness. Similarly, even though the combination of positively correlated effective risk evaluation metrics can outperform most negatively correlated non-effective ones, it still cannot improve the fault localization effectiveness. Furthermore, low-correlated risk evaluation metrics should also be considered for fault localization. The study concluded that getting highly negatively correlated risk evaluation metrics is almost impossible. The combination of such risk evaluation metrics would improve fault localization accuracy.

Background: The usability of university websites is important to ascertain that they serve their intended purpose. Their usability can be evaluated either by testing methods that rely on actual users or by inspection methods that rely on experts for evaluation. Heuristic evaluation and guideline reviews are two inspection methods of usability evaluation. A heuristic evaluation consists of a few general heuristics (rules), which are limited to checking general flaws in the design. A guideline review uses a much larger set of guidelines/suggestions that fit a specific business domain. Literature review: Most of the literature has equated usability studies with testing methods and has given less focus to inspection methods. Moreover, those studies have examined usability in a general sense and not in domain- and culture-specific contexts. Research questions: 1. Do domain- and culture-specific heuristic evaluation and guideline reviews work similarly in evaluating the usability of applications? 2. Which of these methods is better in terms of the nature of evaluation, time needed for evaluation, evaluation procedure, templates adopted, and evaluation results? 3. Which method is better in terms of thoroughness and reliability? Research methodology : This study uses a comparative methodology. The two inspection methods—guideline reviews and heuristic evaluation—are compared in a domain- and the culture-specific context in terms of the nature, time required, approach, templates, and results. Results: The results reflect that both methods identify similar usability issues; however, they differ in terms of the nature, time duration, evaluation procedure, templates, and results of the evaluation. Conclusion: This study contributes by providing insights for practitioners and researchers about the choice of an evaluation method for domain- and culture-specific evaluation of university websites.

Culture fit is an essential consideration while designing websites in this technologically evolving era, yet it is too often ignored in current usability evaluation methods. The heuristic evaluation is one such method, in which, generally, experts use a set of heuristics as a guide. These heuristics are well designed to pinpoint the general usability design faults; however, they limit identifying potential usability problems for specific domains and cultures. This article presents a formal methodology with a sequence of steps of how domain and culture-specific heuristics can be designed. This methodology is validated and applied in the context of websites for universities in Pakistan.