computer Vision (CV) has seen significant advances, and autonomous driving technologies have been transforming the automotive industry in recent years. Autonomous vehicles require robust CV algorithms, while Advanced ...
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We study the behavior of deterministic methods for solving inverse problems in imaging. These methods are commonly designed to achieve two goals: (1) attaining high perceptual quality, and (2) generating reconstructio...
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We study the behavior of deterministic methods for solving inverse problems in imaging. These methods are commonly designed to achieve two goals: (1) attaining high perceptual quality, and (2) generating reconstructions that are consistent with the measurements. We provide a rigorous proof that the better a predictor satisfies these two requirements, the larger its Lipschitz constant must be, regardless of the nature of the degradation involved. In particular, to approach perfect perceptual quality and perfect consistency, the Lipschitz constant of the model must grow to infinity. This implies that such methods are necessarily more susceptible to adversarial attacks. We demonstrate our theory on single image super-resolution algorithms, addressing both noisy and noiseless settings. We also show how this undesired behavior can be leveraged to explore the posterior distribution, thereby allowing the deterministic model to imitate stochastic methods. Copyright 2024 by the author(s)
In the digital age, Voice over Internet Protocol (VoIP) systems have become an indispensable part of modern telecommunications. However, the complex structure of VoIP systems like SIP PBX presents significant challeng...
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The fall causes minor to major injuries in human beings due to accidents, weakness in muscles, loss of balance etc. Therefore, the researchers have given the solutions for various fall detection systems using sensors,...
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The onset of Industry 4.0 is rapidly transforming the manufacturing world through the integration of cloud computing, machine learning (ML), artificial intelligence (AI), and universal network connectivity, resulting ...
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Computing systems face diverse and substantial cybersecurity threats. To mitigate these cybersecurity threats while developing software, engineers need to be competent in the skill of threat modeling. In industry and ...
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Computing systems face diverse and substantial cybersecurity threats. To mitigate these cybersecurity threats while developing software, engineers need to be competent in the skill of threat modeling. In industry and academia, there are many frameworks for teaching threat modeling, but our analysis of these frameworks suggests that (1) these approaches tend to be focused on component-level analysis rather than educating students to reason holistically about a system's cybersecurity, and (2) there is no rubric for assessing a student's threat modeling competency. To address these concerns, we propose using systems thinking in conjunction with popular and industry-standard threat modeling frameworks like STRIDE for teaching and assessing threat modeling competency. Prior studies suggest a holistic approach like systems thinking can be suitable for understanding and mitigating cybersecurity threats. Therefore, the purpose of this study is to develop and pilot two novel rubrics - one for assessing STRIDE threat modeling performance and the other for assessing systems thinking performance while conducting STRIDE. To conduct this study, we piloted the two rubrics mentioned above to assess threat model artifacts of students enrolled in an upper-level softwareengineering course at Purdue University in Fall 2021, Spring 2023, and Fall 2023. Our results reveal that students who had both systems thinking and STRIDE instruction identified and attempted to mitigate component-level as well as systems-level threats. On the other hand, students with only STRIDE instruction tended to focus on identifying and mitigating component-level threats and discounted system-level threats. Our work contributes to the engineering education community by: (1) describing a new rubric for assessing threat modeling based on systems thinking;(2) identifying trends and blindspots in students' threat modeling approach;and (3) envisioning the benefits of integrating systems thinking in threat modeling
The development of softwaresystems is preceded by an important first phase, requirements elicitation, wherein developers establish the intended functionality of a system to be developed in a series of interviews with...
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The paper details the architecture of an automated measurement system application developed in Python. It leverages packages such as PyVISA, Pandas, and PyQt6 for its graphical user interface. The application is utili...
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In the era of data-driven decision-making, the ability to rapidly and accurately analyze vast amounts of textual data is essential. This paper examines the role of text pre-processing techniques in enhancing the effec...
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Artificial Intelligence has demonstrated its significance in softwareengineering through notable improvements in productivity, accuracy, collaboration, and learning outcomes. This study examines the impact of generat...
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