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Euromicro Conference on Software Engineering and Advanced Applications (SEAA)

作者： Michael Vierhauser Kristof Meixner Stefan Biffl Department of Computer Science University of Innsbruck Austria CDL-SQI Vienna Austria Institute of Information Systems Engineering TU Wien Vienna Austria CDP Vienna Austria

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.

关键词： Quality assurance Navigation Data collection Requirements engineering Iterative methods Interviews Testing Drones Software engineering Meteorology

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Social Media as an Instant Source of Feedback on Water Quality

IEEE Transactions on Technology and Society

引用

IEEE Transactions on Technology and Society 2023年第4期4卷 325-333页

作者： Ahmad, K. Ayub, M.A. Ahmad, K. Khan, J. Ahmad, Nasir Al-Fuqaha, A. Jeonbuk National University Department of Information and Electronics Engineering Jeonju54896 Korea Republic of University of Engineering and Technology Peshawar Department of Computer Systems Engineering Peshawar2500 Pakistan Munster Technological University Department of Computer Science Cork021 T12 P928 Ireland AJOU University Department of Artificial Intelligence Suwon16499 Korea Republic of Hamad Bin Khalifa University College of Science and Engineering Doha Qatar

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 automatically analyze and retrieve social media posts relevant to water quality, with particular attention to posts describing different aspects of water quality, such as color, smell, taste, and water-related illnesses. To this aim, we propose a novel framework incorporating different preprocessing, data augmentation, and classification techniques. We use three neural network (NN) architectures for our framework, namely: 1) bidirectional encoder representations from transformers (BERTs);2) robustly optimized BERT pretraining approach (XLM-RoBERTa);and 3) a custom long short-term memory (LSTM) model. These are employed in a merit-based fusion scheme. For merit-based weight assignment to the models, several optimization and search techniques are compared, including a particle swarm optimization (PSO), genetic algorithm (GA), brute force (BF), Nelder-Mead, and Powell's optimization methods. We also provide an evaluation of the individual models where the highest F1-score of 0.81 is obtained with the BERT model. Overall, in merit-based fusion, better results are obtained with BF achieving an F1-score score of 0.852. We also provide a comparison against existing methods, where a significant improvement for our proposed solutions is obtained. We believe such a rigorous analysis of this relatively new topic will provide a baseline for future research. © 2020 IEEE.

关键词： Water quality

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Enhancing 3D Human Pose Estimation Amidst Severe Occlusion with Dual Transformer Fusion

arXiv

引用

arXiv 2024年

作者： Ghafoor, Mehwish Mahmood, Arif Bilal, Muhammad Department of Computer Science Information Technology University Lahore Pakistan Center of Excellence in Intelligent Engineering Systems Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia

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 infer 3D poses from 2D joint observations. This paper introduces a Dual Transformer Fusion (DTF) algorithm, a novel approach to obtain a holistic 3D pose estimation, even in the presence of severe occlusions. Confronting the issue of occlusion-induced missing joint data, we propose a temporal interpolation-based occlusion guidance mechanism. To enable precise 3D Human Pose Estimation, our approach leverages the innovative DTF architecture, which first generates a pair of intermediate views. Each intermediate-view undergoes spatial refinement through a self-refinement schema. Subsequently, these intermediate-views are fused to yield the final 3D human pose estimation. The entire system is end-to-end trainable. Through extensive experiments conducted on the Human3.6M and MPI-INF-3DHP datasets, our method’s performance is rigorously evaluated. Notably, our approach outperforms existing state-of-the-art methods on both datasets, yielding substantial improvements. The code is available here: https://***/MehwishG/DTF. © 2024, CC BY-NC-ND.

关键词： 3D modeling

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Baby Bump: A Monitoring System for Pregnant Mothers and Babies

Baby Bump: A Monitoring System for Pregnant Mothers and Babi...

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International Conference for Convergence of Technology (I2CT)

作者： Dhanuka Balasooriya Sasini Perera Dinuka R. Wijendra Sahani Rathnayaka Rajendra Kishan Karthiga Rajendran Markandu Thirukumar Department of Computer Systems and Software Engineering Sri Lankan Institute of Information Technology Malabe Sri Lanka Department of Obstetrics & Gynaecology Teaching Hospital Batticaloa Sri Lanka

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.

关键词： Pregnancy Pediatrics Planets Pressing Extraterrestrial measurements Chatbots Mobile applications

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Employing Two-Dimensional Word Embedding for Difficult Tabular Data Stream Classification

arXiv

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arXiv 2024年

作者： Zyblewski, Pawel Department of Systems and Computer Networks Faculty of Information and Communication Technology Wroclaw University of Science and Technology Wybrzeże Wyspiańskiego 27 Wroclaw50-370 Poland

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 high imbalance ratio. Consequently, developing new approaches to classifying difficult data streams is a rapidly growing research area. At the same time, the proliferation of deep learning and transfer learning, as well as the success of convolutional neural networks in computer vision tasks, have contributed to the emergence of a new research trend, namely Multi-Dimensional Encoding (MDE), focusing on transforming tabular data into a homogeneous form of a discrete digital signal. This paper proposes Streaming Super Tabular Machine Learning (SSTML), thereby exploring for the first time the potential of MDE in the difficult data stream classification task. SSTML encodes consecutive data chunks into an image representation using the STML algorithm and then performs a single ResNet-18 training epoch. Experiments conducted on synthetic and real data streams have demonstrated the ability of SSTML to achieve classification quality statistically significantly superior to state-of-the-art algorithms while maintaining comparable processing time. © 2024, CC BY-NC-SA.

关键词： Deep learning

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Unveiling the Black Box: Neural Cryptanalysis with XAI

Unveiling the Black Box: Neural Cryptanalysis with XAI

引用

IEEE International Conference on systems, Man and Cybernetics

作者： Yue-Tian Goi Shu-Min Leong Raphaël C.-W. Phan Shangqi Lai Ana Sălăgean School of Information Technology Monash University Distributed Systems Security Group CSIRO's Data61 Australia Department of Computer Science Loughborough University UK

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.

关键词： Deep learning Adaptation models Ciphers Correlation Additives Explainable AI Closed box Manuals Encryption Cybernetics

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Behavioral Cybersecurity: Dynamic Persuasive Strategies to Enhance Password Security

Behavioral Cybersecurity: Dynamic Persuasive Strategies to E...

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International Conference of computer and Informatics Engineering (IC2IE)

作者： Husam Lahza Badr Alsamani Information Technology Department. Faculty of Computing and Information Technology King Abdulaziz University (KAU) Jeddah Saudi Arabia Information Systems Department. College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh Saudi Arabia

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.

关键词： Meters Knowledge engineering Computational modeling Psychology Passwords Organizations computer crime Informatics

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An Adaptive Decision-Making Approach for Better Selection of Blockchain Platform for Health Insurance Frauds Detection with Smart Contracts: Development and Performance Evaluation 14

An Adaptive Decision-Making Approach for Better Selection of...

引用

14th International Conference on Ambient systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023

作者： Kaafarani, Rima Ismail, Leila Zahwe, Oussama ICCS-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 a suitable one for a specific type of business. This paper presents a classification of over one hundred blockchain platforms. We develop smart contracts for detecting healthcare insurance frauds using the top two blockchain platforms selected based on our proposed decision-making map approach which selects the top suitable platforms for healthcare insurance frauds detection application. Our classification shows that the largest percentage of platforms can be used for all types of application domains, the second biggest percentage for financial services, and a small number is to develop applications in specific domains. Our decision-making map and performance evaluations reveal that Hyperledger Fabric surpassed Neo in all metrics for detecting healthcare insurance frauds. © 2023 Elsevier B.V.. All rights reserved.

关键词： Smart contract

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Dynamic Programming Algorithm using Furniture Industry 0/1 Knapsack Problem

Dynamic Programming Algorithm using Furniture Industry 0/1 K...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Mietha Anditta Natashya Amartya Laurens Spits Warnars Harco Leslie Hendric Spits Warnars Arief Ramadhan Teddy Siswanto Teddy Mantoro Nurulhuda Noordin Computer Science Departement School of Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Departement School of Information Systems Bina Nusantara University Jakarta Indonesia Computer Science Departement BINUS Graduate Program - Computer Science Bina Nusantara University Jakarta Indonesia PJJ Informatika Telkom University Bandung Indonesia Information Systems Department Trisakti University Jakarta Indonesia Faculty of Engineering and Technology Sampoerna University Jakarta Indonesia School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Malaysia

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.

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Leveraging Explainable AI for Actionable Insights in IoT Intrusion Detection

Leveraging Explainable AI for Actionable Insights in IoT Int...

引用

IEEE International Conference on System of systems Engineering (SoSE)

作者： Sohan Gyawali Jiaqi Huang Yili Jiang Department of Technology Systems East Carolina University Greenville North Carolina Department of Computer Science and Cybersecurity University of Central Missouri Warrensburg Missouri Department of Computer and Information Science University of Mississippi University Mississippi

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.

关键词： Deep learning Analytical models Explainable AI Intrusion detection Faces Cyberattack System of systems

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