Fatigue during driving significantly impairs a driver's reaction time, awareness, and decision-making abilities, leading to an increased risk of accidents. Recognising and mitigating driver fatigue is crucial for ...
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Depression is a prevalent mental disorder affecting behavior, cognition, and physical activity. Early detection and intervention are crucial to mitigating its severe impacts, including suicidal ideation or suicide att...
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Software bug classification is a critical task in software engineering aimed at identifying defects early to improve software quality and reliability. Despite its importance, effectively classifying software defects r...
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The process of a supply chain involves a series of activities that distribute, assemble, and manage products, transferring them from the supplier to the end consumer. It operates as a complex, interconnected network r...
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Generative Adversarial Networks (GAN) and their several variants have not only been used for adversarial purposes but also used for extending the learning coverage of different AI/ML models. Most of these variants are...
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Generative Adversarial Networks (GAN) and their several variants have not only been used for adversarial purposes but also used for extending the learning coverage of different AI/ML models. Most of these variants are unconditional and do not have enough control over their outputs. Conditional GANs (CGANs) have the ability to control their outputs by conditioning their generator and discriminator with an auxiliary variable (such as class labels, and text descriptions). However, CGANs have several drawbacks such as unstable training, non-convergence and multiple mode collapses like other unconditional basic GANs (where the discriminators are classifiers). DCGANs, WGANs, and MMDGANs enforce significant improvements to stabilize the GAN training although have no control over their outputs. We developed a novel conditional Wasserstein GAN model, called CWGAN (a.k.a RD-GAN named after the initials of the authors' surnames) that stabilizes GAN training by replacing relatively unstable JS divergence with Wasserstein-1 distance while maintaining better control over its outputs. We have shown that the CWGAN can produce optimal generators and discriminators irrespective of the original and input noise data distributions. We presented a detailed formulation of CWGAN and highlighted its salient features along with proper justifications. We showed the CWGAN has a wide variety of adversarial applications including preparing fake images through a CWGAN-based deep generative hashing function and generating highly accurate user mouse trajectories for fooling any underlying mouse dynamics authentications (MDAs). We conducted detailed experiments using well-known benchmark datasets in support of our claims. IEEE
Data is often thought of and treated as a non-rival good, which would imply that one person’s use of data does not inherently diminish its availability for others. Building on research in privacy and statistics, we a...
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This paper proposes a very novel and dynamic approach to psychological assessment by utilising virtual video games as a psychometric tool. Traditional methods, such as interviews and surveys, often yield conscious res...
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Agriculture is one of the biggest consumers of fresh water. Different types of irrigation systems are available and used in agricultural greenhouses. These irrigation systems are traditional, which do not allow water ...
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The inside-outside algorithm is widely utilized in statistical models related to context-free grammars. It plays a key role in the EM estimation of probabilistic context-free grammars. In this work, we introduce an in...
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