Testing and validating Cyber-Physical systems (CPSs) in the aerospace domain, such as field testing of drone rescue missions, poses challenges due to volatile mission environments, such as weather conditions. While te...
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ISBN:
(数字)9798350380262
ISBN:
(纸本)9798350380279
Testing and validating Cyber-Physical systems (CPSs) in the aerospace domain, such as field testing of drone rescue missions, poses challenges due to volatile mission environments, such as weather conditions. While testing processes and methodologies are well established, structured guidance and execution support for field tests are still weak. This paper identifies requirements for field testing of drone missions and introduces the Field Testing Scenario Management (FiTS) approach for adaptive field testing guidance. FiTS aims to provide sufficient guidance for field testers as a foundation for efficient data collection to facilitate quality assurance and iterative improvement of field tests and CPSs. FiTS shall leverage concepts from scenario-based requirements engineering and Behavior-Driven Development to define structured and reusable test scenarios, with dedicated tasks and responsibilities for role-specific guidance. We evaluate FiTS by (i) applying it to three use cases for a search-and-rescue drone application to demonstrate feasibility and (ii) interviews with three experienced drone developers to assess its usefulness and collect further requirements. The study results indicate FiTS to be feasible and useful to facilitate drone field testing and data analysis.
This article focuses on an important environmental challenge: the measurement of water quality, by analyzing the potential of social media to be harnessed as an immediate source of feedback. The goal of the work is to...
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In the field of 3D Human Pose Estimation from monocular videos, the presence of diverse occlusion types presents a formidable challenge. Prior research has made progress by harnessing spatial and temporal cues to infe...
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In contemporary times, nations like Sri Lanka are actively enhancing their efforts to improve the life expectancy of their citizens, with a strong focus on public health. The relationship between health and life expec...
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ISBN:
(数字)9798350394474
ISBN:
(纸本)9798350394481
In contemporary times, nations like Sri Lanka are actively enhancing their efforts to improve the life expectancy of their citizens, with a strong focus on public health. The relationship between health and life expectancy is pivotal. Poor health conditions tend to diminish life expectancy, while robust health measures tend to extend it. Within the context of health, birth and death events are of paramount importance. Birth represents the genesis of life, enabling individuals to contribute to future generations and the overall population's expansion. It's the cornerstone of life on our planet. However, modern times have witnessed challenges in pregnancy and childbirth, with some infants not surviving the prenatal period or being born with health complications. Consequently, there is a pressing need to present a solution that can contribute to the betterment of maternal and infant health, ultimately augmenting Sri Lanka's life expectancy. The proposed solution is the development of a mobile application, encompassing features such as a Nutrition Predictor, Medicine Effect Predictor, AI chatbot, and Baby Status Predictor. This innovative application aims to address the complex issues surrounding pregnancy and childbirth, with the overarching goal of improving the nation's life expectancy.
Rapid technological advances are inherently linked to the increased amount of data, a substantial portion of which can be interpreted as data stream, capable of exhibiting the phenomenon of concept drift and having a ...
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At CRYPTO'19, Gohr[1] presented ResNet-based neural distinguishers (ND) for the round-reduced SPECK32/64 cipher. However, due to the black-box use of such deep learning models, it is hard for humans to understand ...
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ISBN:
(数字)9781665410205
ISBN:
(纸本)9781665410212
At CRYPTO'19, Gohr[1] presented ResNet-based neural distinguishers (ND) for the round-reduced SPECK32/64 cipher. However, due to the black-box use of such deep learning models, it is hard for humans to understand why these distinguishers work, impeding advancements in cryptanalytic knowledge. In this work, we aim to effectively adapt eXplainable Artificial Intelligence (XAI) techniques, notably Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP), to gain a detailed understanding of the important features useful in Gohr's neural distinguishers.
This paper introduces dynamic, persuasive strategies within the realm of behavioral cybersecurity to combat the persistent issue of password vulnerabilities. Utilizing the principles of the Fogg Behavioral Model, we d...
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ISBN:
(数字)9798331505530
ISBN:
(纸本)9798331505547
This paper introduces dynamic, persuasive strategies within the realm of behavioral cybersecurity to combat the persistent issue of password vulnerabilities. Utilizing the principles of the Fogg Behavioral Model, we delve into the psychological underpinnings that influence user behavior in digital security practices. Our study offers a novel approach by integrating behavioral science into cybersecurity, specifically in password creation, and presents both theoretical insights and practical implementations. The originality of this study lies in its unique application of the Fogg Behavioral Model to enhance password strength through tailored interventions. Theoretical implications include expanding the application of persuasive technology in cybersecurity, while managerial implications suggest actionable strategies for organizations to improve password practices among users. By providing empirical insights and practical implementations, we demonstrate how tailored persuasive tactics can significantly alleviate the risks of weak passwords, thereby contributing to a more secure cyber environment.
作者:
Kaafarani, RimaIsmail, LeilaZahwe, OussamaICCS-Lab
Computer Science Department American University of Culture and Education Beirut1507 Lebanon Laboratory
School of Computing and Information Systems The University of Melbourne Melbourne Australia Laboratory
Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Abu Dhabi United Arab Emirates National Water and Energy Center
United Arab Emirates University Abu Dhabi United Arab Emirates
Blockchain technology has piqued the interest of businesses of all types, while consistently improving and adapting to business requirements. Several blockchain platforms have emerged, making it challenging to select ...
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Dynamic programming is a fundamental algorithm that can be found in our daily lives easily. One of the dynamic programming algorithm implementations consists of solving the 0/1 knapsack problem. A 0/1 knapsack problem...
Dynamic programming is a fundamental algorithm that can be found in our daily lives easily. One of the dynamic programming algorithm implementations consists of solving the 0/1 knapsack problem. A 0/1 knapsack problem can be seen from industrial production cost. It is prevalent that a production cost has to be as efficient as possible, but the expectation is to get the proceeds of the products higher. Thus, the dynamic programming algorithm can be implemented to solve the diverse knapsack problem, one of which is the 0/1 knapsack problem, which would be the main focus of this paper. The implementation was implemented using C language. This paper was created as an early implementation algorithm using a Dynamic program algorithm applied to an Automatic Identification System (AIS) dataset.
The rise of IoT networks has heightened the risk of cyber attacks, necessitating the development of robust detection methods. Although deep learning and complex models show promise in identifying sophisticated attacks...
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ISBN:
(数字)9798350365917
ISBN:
(纸本)9798350365924
The rise of IoT networks has heightened the risk of cyber attacks, necessitating the development of robust detection methods. Although deep learning and complex models show promise in identifying sophisticated attacks, they face challenges related to explainability and actionable insights. In this investigation, we explore and contrast various explainable AI techniques, including LIME, SHAP, and counterfactual explanations, that can be used to enhance the explainability of intrusion detection outcomes. Furthermore, we introduce a framework that utilizes counterfactual SHAP to not only provide explanations but also generate actionable insights for guiding appropriate actions or automating intrusion response systems. We validate the effectiveness of various models through meticulous analysis within the CICIoT2023 dataset. Additionally, we perform a comparative evaluation of our proposed framework against previous approaches, demonstrating its ability to produce actionable insights.
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