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Trustworthy Sensor Fusion Against Inaudible Command Attacks in Advanced Driver-Assistance Systems

IEEE Internet of Things Journal 2023年第19期10卷 17254-17264页

作者： Guan, Jiwei Pan, Lei Wang, Chen Yu, Shui Gao, Longxiang Zheng, Xi Macquarie University School of Computing SydneyNSW2109 Australia CSIRO Data61 SydneyNSW1710 Australia Deakin University School of Information Technology Waurn PondsVIC3125 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Qilu University of Technology Shangdong Computer Science Center Jinan250316 China

There are increasing concerns about malicious attacks on autonomous vehicles. In particular, inaudible voice command attacks pose a significant threat as voice commands become available in autonomous driving systems. How to empirically defend against these inaudible attacks remains an open question. Previous research investigates utilizing deep learning-based multimodal fusion for defense, without considering the model uncertainty in trustworthiness. As deep learning has been applied to increasingly sensitive tasks, uncertainty measurement is crucial in helping improve model robustness, especially in mission-critical scenarios. In this article, we propose the multimodal fusion framework (MFF) as an intelligent security system to defend against inaudible voice command attacks. MFF fuses heterogeneous audio-vision modalities using VGG family neural networks and achieves the detection accuracy of 92.25% in the comparative fusion method empirical study. Additionally, extensive experiments on audio-vision tasks reveal the model's uncertainty. Using expected calibration errors, we measure calibration errors and Monte Carlo Dropout to estimate the predictive distribution for the proposed models. Our findings show empirically to train robust multimodal models, improve standard accuracy and provide a further step toward interpretability. Finally, we discuss the pros and cons of our approach and its applicability for advanced driver assistance systems. © 2014 IEEE.

关键词： Speech recognition

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Contrastive Learning for Chest X-ray Classification: A Fusion of Topological data Analysis and ResNet 9

Contrastive Learning for Chest X-ray Classification: A Fusio...

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9th IEEE International Conference on data science in Cyberspace, DSC 2024

作者： Ren, Hao Luo, Zeyu Jing, Fengshi Zhang, Xinyue He, Han Yu, Yonghao Zhao, Dawei City University of Macau Faculty of Data Science China Minzu University of China Beijing China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350391367

Lung diseases such as pneumonia and tuberculosis present significant global health challenges, leading to millions of deaths annually. Chest X-ray imaging is crucial for diagnosing these conditions, but interpretation often relies on specialized radiologists, creating barriers in resource-limited areas. This study introduces a novel framework integrating topological data analysis (TDA), deep learning with ResNet-50, and contrastive learning to enhance the accuracy and computational efficiency of chest X-ray analysis. TDA captures global topological structures, while ResNet-50 extracts detailed local features. Contrastive learning then optimizes the feature space, enhancing the model's discriminative power and generalization capabilities. Comprehensive evaluations on multiple datasets demonstrate the framework's robustness and effectiveness, showing substantial improvements in diagnostic performance and computational efficiency. These findings suggest that the proposed approach is well-suited for real-time clinical applications, especially in resource-limited settings. Future work will focus on further optimizing the model and exploring its application to other medical imaging modalities, broadening its clinical utility. © 2024 IEEE.

关键词： Contrastive Learning

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An Exploratory Study of Automated Anti-phishing System 9th

An Exploratory Study of Automated Anti-phishing System

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9th International Conference on computing and Informatics, ICOCI 2023

作者： Aziz, Mochamad Azkal Azkiya Riskhan, Basheer Zakaria, Nur Haryani Jambli, Mohamad Nazim Albukhary International University Kedah Alor Setar05200 Malaysia Data Management & Software Solution Research Lab School of Computing Universiti Utara Malaysia Kedah Sintok06010 Malaysia Faculty of Computer Science and Information Technology University Malaysia Sarawak Sarawak Kota Samarahan Malaysia

ISBN: (纸本)9789819995882

Phishing attacks have emerged as a major problem in the digital world due to a rising trend in their frequency. While various approaches have been developed to detect and prevent phishing attacks, a definitive solution to the problem has yet to be discovered. This study discusses automated anti-phishing systems while analyzing and comparing various anti-phishing strategies using exploratory research. Traditional, machine learning, and deep learning-based anti-phishing systems are discussed in the article. The study highlights the use of Artificial Intelligence (AI) based systems, particularly utilizing methods such as Convolutional Neural Networks, Support Vector Machines, and Recurrent Neural Networks. These AI-based approaches dominate the current trend in the field. This study could potentially be helpful for researchers who wish to delve deeper into the topic of automated phishing detection and prevention systems with a comprehensive review. It is advised to carry out further research to investigate the strengths and limitations of different methods and algorithms used in automated anti-phishing systems to understand their performance and effectiveness better. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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Explainable AI in Handwriting Detection for Dyslexia Using Transfer Learning

arXiv

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arXiv 2024年

作者： Robaa, Mahmoud Balat, Mazen Awaad, Rewaa Omar, Esraa Aly, Salah A. CS & IT Dept. Egypt-Japanese University of Science & Tech. Alexandria Egypt Computer Science Faculty of Science Fayoum University Fayoum Egypt Faculty of Computing & Data Science Badya University Giza Egypt

This study introduces an explainable AI (XAI) framework for the detection of dyslexia through handwriting analysis, achieving an impressive test precision of 99.65%. The framework integrates transfer learning and transformer-based models, identifying handwriting features associated with dyslexia while ensuring transparency in decision-making via Grad-CAM visualizations. Its adaptability to different languages and writing systems underscores its potential for global applicability. By surpassing the classification accuracy of state-of-the-art methods, this approach demonstrates the reliability of handwriting analysis as a diagnostic tool. The findings emphasize the framework’s ability to support early detection, build stakeholder trust, and enable personalized educational strategies. © 2024, CC BY.

关键词： Federated learning

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When Artificial Intelligence meets Navarasas - Facial Expression Classification in Kathakali Using DSFD and CNN

When Artificial Intelligence meets Navarasas - Facial Expres...

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2024 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems, SPICES 2024

作者： Iyer, Ganesh Neelakanta Saengarunvong, Nattapong Le, Minh Hien School of Computing National University of Singapore Department of Computer Science Singapore Faculty of Engineering King Mongkut's University of Technology Department of Computer Engineering Thonburi Bangkok Thailand Case School of Engineering Case Western Reserve University Department of Computer and Data Sciences ClevelandOH United States

ISBN: (纸本)9798350376135

Kathakali, an ancient art form that dates back to the 17th century, is renowned for its intricate hand gestures, dance movements, musical accompaniment, elaborate costumes, and makeup. Mastering such an art form is a challenge even for the most dedicated enthusiasts, given its unconventional methods of expression. However, with current advanced technology, we can use this to aid the learning of unfamiliar subjects by classifying factors to help the new learners. In this paper, we focus on classifying one aspect of the play, which is the expressions on the actor's face given a picture using computer vision methods. To achieve the final model, we use a combination of Dual Shot Face Detector (DSFD) and Convolution Neural Network (CNN) and finally tuning up the epochs of the training process. With our dataset, the proposed model reached an accuracy of 93% among nine classes. This project hopes to introduce Kathakali to a wider audience and facilitate the learning process of this art form. © 2024 IEEE.

关键词： Convolutional neural networks

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Improved Dwarf Mongoose Optimization for Constrained Engineering Design Problems

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Journal of Bionic Engineering 2023年第3期20卷 1263-1295页

作者： Jeffrey O.Agushaka Absalom E.Ezugwu Oyelade N.Olaide Olatunji Akinola Raed Abu Zitar Laith Abualigah School of Mathematics Statisticsand Computer ScienceUniversity of KwaZulu-NatalKing Edward AvenuePietermaritzburg CampusPietermaritzburg3201KwaZulu-NatalSouth Africa Department of Computer Science Federal University of LafiaLafia950101Nigeria Unit for Data Science and Computing North-West University11 Hoffman StreetPotchefstroom2520South Africa Sorbonne Center of Artificial Intelligence Sorbonne University-Abu Dhabi38044Abu DhabiUnited Arab Emirates Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan Faculty of Information Technology Middle East UniversityAmman11831Jordan Faculty of Information Technology Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains Malaysia11800Pulau PinangMalaysia

This paper proposes a modified version of the Dwarf Mongoose Optimization Algorithm (IDMO) for constrained engineering design problems. This optimization technique modifies the base algorithm (DMO) in three simple but effective ways. First, the alpha selection in IDMO differs from the DMO, where evaluating the probability value of each fitness is just a computational overhead and contributes nothing to the quality of the alpha or other group members. The fittest dwarf mongoose is selected as the alpha, and a new operator ω is introduced, which controls the alpha movement, thereby enhancing the exploration ability and exploitability of the IDMO. Second, the scout group movements are modified by randomization to introduce diversity in the search process and explore unvisited areas. Finally, the babysitter's exchange criterium is modified such that once the criterium is met, the babysitters that are exchanged interact with the dwarf mongoose exchanging them to gain information about food sources and sleeping mounds, which could result in better-fitted mongooses instead of initializing them afresh as done in DMO, then the counter is reset to zero. The proposed IDMO was used to solve the classical and CEC 2020 benchmark functions and 12 continuous/discrete engineering optimization problems. The performance of the IDMO, using different performance metrics and statistical analysis, is compared with the DMO and eight other existing algorithms. In most cases, the results show that solutions achieved by the IDMO are better than those obtained by the existing algorithms.

关键词： Improved dwarf mongoose Nature-inspired algorithms Constrained optimization Unconstrained optimization Engineering design problems

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A Review on Solar Panel Adoption in Residential Malaysia: Trends, Benefits and Technologies 2nd

A Review on Solar Panel Adoption in Residential Malaysia: Tr...

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Quan, Teoh Chee Jasser, Muhammed Basheer Issa, Bayan Ajibade, Samuel-Soma M. Majeed, Anwar P. P. Abdul Luo, Yang Department of Smart Computing and Cyber Resilience School of Computing and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Petaling Jaya Selangor Sunway City47500 Malaysia Department of Data Science and Artificial Intelligence School of Computing and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Petaling Jaya47500 Malaysia Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Petaling Jaya47500 Malaysia Faculty of Informatics Engineering University of Aleppo Aleppo Syria School of Intelligent Manufacturing Ecosystem Xi’an Jiaotong-Liverpool University Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

This survey provides detailed perspectives on Malaysian solar energy system adoption in relation to the economic outcome and environmental and policy context. This work has been done by using comprehensive keyword lists and inclusion criteria to confirm that the findings of this survey capture as many relevant publications as possible from the research period from 2010 to 2024. Unlike numerous papers that investigate such systems with mere commercial or industrial perspective, this survey aims at assessing residential installation, and providing accurate cost benefit analysis while showing the business belief of such schemes. It also examines various opinions, especially the Malaysian government policies and strategic incentives and staking its success on public awareness and acceptance. We equally allocate a considerable portion of this survey to discussing the environmental effects and the life cycle analyses comparing solar energy with other energy types. Notably, the survey is also under sustainable development goal 7 with the goal of achieving universal access to affordable reliable, sustainable and modern energy. Such a focus also makes the identified topics relevant for Malaysia, which is useful for references in subsequent projects and activities. These would include use of web-survey, the coverage on the residential sector, the ability to incorporate policy analysis and emphasis on sustainability to the study on solar energy usage in residential Malaysia. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Clean energy

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Using Arduino Iot Modules As A Low Cost Environmental Research Monitoring System 1

Using Arduino Iot Modules As A Low Cost Environmental Resear...

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1st International Conference on Computer science and Emerging Technologies, CSET 2023

作者： Magableh, Murad Marie, Ziyad Mohamed, Rajina R. Bin Ibrahim, Mohd Hafizuddin Jusoh, Julaily Aida Kumar, Rohit Irbid National University Faculty of Business and Financial Science MIS BI and Data Analysis Department Irbid Jordan Al-Istiqlal University Faculty of Administrative Sciences and Informatics Public Administration Department Palestine College of Computing Dan Informatics Universiti Tenaga Nasional Malaysia Politeknik Kuala Terenngganu Department of Electrical Engineering Kuala Terengganu20200 Malaysia UniSZA Faculty of Informatics and Computing Malaysia School of Computer Science and Engineering Jain Deemed to Be University Bangalore India

ISBN: (纸本)9798350341737

Environmental monitoring plays a crucial role in understanding the impact of human activities on our ecosystems. However, the cost and complexity of traditional monitoring systems often hinder widespread deployment, especially in resource-constrained regions. This research paper explores the feasibility and effectiveness of employing Arduino Internet of Things (IoT) modules as a low-cost alternative for environmental research monitoring. The study focuses on designing and implementing an Arduino-based environmental monitoring system capable of gathering real-time data on key environmental parameters, such as temperature, humidity, air quality, and soil moisture. The hardware setup comprises Arduino microcontrollers interfaced with a range of environmental sensors and connectivity modules for data transmission. To assess the system's reliability and accuracy, a series of field experiments were conducted in diverse environmental settings, including urban areas, agricultural zones, and natural habitats. The collected data was compared against measurements from conventional monitoring equipment to validate the Arduino-based system's performance. The research demonstrates that the Arduino IoT modules can effectively monitor and transmit environmental data with a reasonable level of accuracy and stability, making them suitable for various environmental research applications. The low-cost nature of Arduino components and the open-source ecosystem facilitate customization and scalability, enabling researchers and organizations to develop tailored monitoring solutions for specific environmental contexts. Additionally, the paper discusses the challenges and limitations encountered during the deployment of Arduino-based monitoring systems, including power constraints, sensor calibration, and data processing techniques. Furthermore, it explores potential avenues for improvement and optimization to enhance the system's capabilities and data accuracy. In conclusion, this research

关键词： Internet of things

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An Efficient Algorithm for Software Reliability Prediction via Harris Hawks Optimization 2nd

An Efficient Algorithm for Software Reliability Prediction v...

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Kong, Li Sheng Jasser, Muhammed Basheer Issa, Bayan Ajibade, Samuel-Soma M. Majeed, Anwar P. P. Abdul Luo, Yang Department of Smart Computing and Cyber Resilience School of Computing and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Petaling Jaya47500 Malaysia Department of Data Science and Artificial Intelligence School of Computing and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Petaling Jaya47500 Malaysia Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Petaling Jaya47500 Malaysia Faculty of Informatics Engineering University of Aleppo Aleppo Syria School of Intelligent Manufacturing Ecosystem Xi’an Jiaotong-Liverpool University Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

Software reliability has become an increasingly important characteristic as software systems continue to be integrated into all parts of our lives. While the occurrence of failure after a period of time is within the nature of all software systems, the ability to estimate and predict failure is greatly beneficial to software development and maintenance. Hence, Software Reliability Growth Models (SRGMs) were developed to fulfill this requirement. Conventional parameter estimation methods of SRGM functions were complex and left more to be desired. Consequently, metaheuristics such as Swarm Intelligence algorithms were introduced to estimate and optimize the parameters of SRGM functions. Likewise, Swarm Intelligence algorithms were found to be exceptionally effective in estimating and optimizing the parameters of SRGM functions. This paper presents a novel approach to parameter estimation on three different SRGM functions through Harris’ Hawks Optimization (HHO). The proposed HHO design is compared against numerous different variants of Swarm Intelligence algorithms and is shown to be significantly more efficient than the majority of the compared designs. Moreover, as the proposed design is a base variant of Swarm Intelligence algorithms, various avenues of future improvements have also been presented to improve its overall prediction accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Harris’ Hawks Optimization Parameter Estimation Software Reliability Growth Models Software Reliability Prediction Swarm Intelligence

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Exploring Hydrogen Supply/Demand Networks: Modeller and Domain Expert Views 29

Exploring Hydrogen Supply/Demand Networks: Modeller and Doma...

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29th International Conference on Principles and Practice of Constraint Programming, CP 2023

作者： Klapperstueck, Matthias de Nijs, Frits Senthooran, Ilankaikone Lee-Kopij, Jack de la Banda, Maria Garcia Wybrow, Michael Department of Human-Centred Computing Faculty of IT Monash University ClaytonVIC Australia Department of Data Science and AI Faculty of IT Monash University ClaytonVIC Australia ARC Industrial Training and Transformation Centre OPTIMA CarltonVIC Australia Woodside Energy Ltd. Perth Australia

ISBN: (纸本)9783959773003

Energy companies are considering producing renewable fuels such as hydrogen/ammonia. Setting up a production network means deciding where to build production plants, and how to operate them at minimum electricity and transport costs. These decisions are complicated by many factors including the difficulty in obtaining accurate current data (e.g., electricity price and transport costs) for potential supply locations, the accuracy of data predictions (e.g., for demand and costs), and the need for some decisions to be made due to external (not modelled) factors. Thus, decision-makers need access to a user-centric decision system that helps them visualise, explore, interact and compare the many possible solutions of many different scenarios. This paper describes the system we have built to support our energy partner in making such decisions, and shows the advantages of having a graphical user-focused interactive tool, and of using a high-level constraint modelling language (MiniZinc) to implement the underlying model. © Matthias Klapperstueck, Frits de Nijs, Ilankaikone Senthooran, Jack Lee-Kopij, Maria Garcia de la Banda, and Michael Wybrow.

关键词： Modeling languages

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