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TechRxiv 2022年

作者： Wang, Haiyan Li, Yuan He, Ying Luo, Fucai Yu, F. Richard Ming, Zhong Wang, Shulan The College of Computer Science and Software Engineering Shenzhen University Shenzhen China The Department of New Networks Pengcheng Laboratory Shenzhen China The Department of Systems and Computer Engineer Carleton University OttawaON Canada The College of Big Data and Internet Shenzhen Technology University Shenzhen China

With the increasing popularity of smart terminals, the Internet of Things (IoT) vastly expands the influence of information technology and creates great values to our society. The Internet of Medical Things (IoMT) is a typical application of the IoT specifically for medical and healthcare related purposes. In IoMT, personal health records (PHRs) generated by intelligent equipment are usually stored in a cloud sever managed by a semi-trusted cloud provider. However, there is still a possibility of the exposure of private information to unauthorized parties. In order to protect a patient’s private information, and ensure that doctors can search a patient’s PHRs without decrypting all ciphertexts, an efficient searchable encryption framework with weighted keywords is proposed in this paper. In this framework, we implement a weighted keyword model to raise the accuracy of search results, and reduce the computational and storage overhead. Besides, compared with the existing schemes, the framework is supportable of multi-search mechanism, which is adaptable to more application scenarios. Finally, the efficiency and security of this framework is proved through security and performance analysis in this paper. © 2022, CC BY.

关键词： Cryptography

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Assurance monitoring of learning enabled cyber-physical systems using inductive conformal prediction based on distance learning

arXiv

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arXiv 2021年

作者： Boursinos, Dimitrios Koutsoukos, Xenofon Department of Electrical Engineering and Computer Science Institute for Software Integrated Systems Vanderbilt University NashvilleTN United States

Machine learning components such as deep neural networks are used extensively in Cyber-Physical systems (CPS). However, such components may introduce new types of hazards that can have disastrous consequences and need to be addressed for engineering trustworthy systems. Although deep neural networks offer advanced capabilities, they must be complemented by engineering methods and practices that allow effective integration in CPS. In this paper, we proposed an approach for assurance monitoring of learning-enabled CPS based on the conformal prediction framework. In order to allow real-time assurance monitoring, the approach employs distance learning to transform high-dimensional inputs into lower size embedding representations. By leveraging conformal prediction, the approach provides well-calibrated confidence and ensures a bounded small error rate while limiting the number of inputs for which an accurate prediction cannot be made. We demonstrate the approach using three data sets of mobile robot following a wall, speaker recognition, and traffic sign recognition. The experimental results demonstrate that the error rates are well-calibrated while the number of alarms is very small. Further, the method is computationally efficient and allows real-time assurance monitoring of CPS. © 2021, CC BY.

关键词： Embedded systems

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Comparative Analysis of Machine Learning Models for Students’ Performance Prediction

Comparative Analysis of Machine Learning Models for Students...

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International Conference on Advances in Digital Science, ICADS 2021

作者： Ismail, Leila Materwala, Huned Hennebelle, Alain Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi15551 United Arab Emirates Al Ain Abu Dhabi United Arab Emirates

ISBN: (纸本)9783030717810

Machine learning for education is an emerging discipline where a model is developed based on training data to make predictions on students’ performance. The main aim is to identify students who would have difficulty in their learning and to take precautionary measures to help them. In this paper, we conduct a comparative analysis of the most used machine learning classification models in the literature. We evaluate the performance of the models in terms of accuracy, F-measure, and execution time using two real-life education datasets. The performance of the models is data-driven. We give insights into the models’ performance and advise on the best model to use accordingly. We believe the results of this paper will be widely used by education professionals for accurate predictions. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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Efficient Resource Allocation and User Association in NOMA-Enabled Vehicular-Aided HetNets with High Altitude Platforms

arXiv

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arXiv 2024年

作者： Nauman, Ali Maashi, Mashael Alkahtani, Hend K. Al-Wesabi, Fahd N. Aljehane, Nojood O. Assiri, Mohammed Ibrahim, Sara Saadeldeen Khan, Wali Ullah The Department of Information and Communication Engineering Yeungnam University Korea Republic of The Department of Software Engineering College of Computer and Information Sciences King Saud University Po box 103786 Riyadh11543 Saudi Arabia The Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia The Department of Computer Science College of Science & Art at Mahayil King Khalid University Saudi Arabia The Department of Computer Science Faculty of Computers and Information Technology University of Tabuk Tabuk Saudi Arabia The Department of Computer Science College of Sciences and Humanities- Aflaj Prince Sattam bin Abdulaziz University Aflaj 16273 Saudi Arabia The Department of Computer and Self Development Preparatory Year Deanship Prince Sattam bin Abdulaziz University Al-Kharj Saudi Arabia University of Luxembourg Luxembourg

The increasing demand for massive connectivity and high data rates has made the efficient use of existing spectrum resources an increasingly challenging problem. Non-orthogonal multiple access (NOMA) is a potential solution for future heterogeneous networks (HetNets) due to its high capacity and spectrum efficiency. In this study, we analyze an uplink NOMA-enabled vehicular-aided HetNet, where multiple vehicular user equipment (VUEs) share the access link spectrum, and a high-altitude platform (HAP) communicates with roadside units (RSUs) through a backhaul communication link. We propose an improved algorithm for user association that selects VUEs for HAPs based on channel coefficient ratios and terrestrial VUEs based on a caching-state backhaul communication link. The joint optimization problems aim to maximize a utility function that considers VUE transmission rates and cross-tier interference while meeting the constraints of backhaul transmission rates and QoS requirements of each VUE. The joint resource allocation optimization problem consists of three sub-problems: bandwidth allocation, user association, and transmission power allocation. We derive a closed-form solution for bandwidth allocation and solve the transmission power allocation sub-problem iteratively using Taylor expansion to transform a non-convex term into a convex one. Our proposed three-stage iterative algorithm for resource allocation integrates all three sub-problems and is shown to be effective through simulation results. Specifically, the results demonstrate that our solution achieves performance improvements over existing approaches. Copyright © 2024, The Authors. All rights reserved.

关键词： Heterogeneous networks

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Cyberbullying Classification Using a Natural Language Processing and Machine Learning Techniques

Cyberbullying Classification Using a Natural Language Proces...

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Advances in Computing Research on Science engineering and Technology (ACROSET), International Conference on

作者： Nikhilesh Pramod Mankar Tushar Mahure Anushka A. Joshi Sagar Dhanraj Pande Aditya Khamparia Ankita Shrikrushna Nathe Fadi Al Turjman School of Engineering and Technology Pimpri Chinchwad University (PCU) Pune India Independent Researcher Amravati India Department of Computer Science Babasaheb Bhimrao Ambedkar University Amethi India Department of Information Technology Kavikulguru Institute of Technology and Science Nagpur India Artificial Intelligence Software Information Systems Engineering Departments AI and Robotics Institute Near East University Nicosia Turkey Research Center for AI and IoT Faculty of Engineering University of Kyrenia Kyrenia Turkey

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

The usage of online entertainment has increased dramatically after some time with the enhancement of the Internet and has turned into the most compelling systems administration stage in this century. Notwithstanding, the upgrade of a social network regularly makes adverse consequences on society that add to two or three terrible peculiarities, for example, online maltreatment, badgering cyberbullying, cybercrime, and internet savaging. Cyberbullying habitually prompts genuine mental and actual misery, especially for ladies and kids, and even occasionally make them to use self-destruction methods. Multiple number of episodes have as of late happened worldwide because of online badgering, like sharing private talks, bits of hearsay, and sexual comments. In this way, the distinguishing proof of harassing messages or messages via online entertainment has gained a significant measure for development for consideration with in scientists. The ground or motive for this exploration is to create a full-fledged way or a method to differentiate online harmful and tormenting drafts by combining various methods and techniques in the domain of natural language processing and AI

关键词： Cyberbullying Entertainment industry Machine learning Gain measurement Natural language processing Internet Bayes methods computer crime

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Hyperparameter optimization in cardio vascular disease prediction

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AIP Conference Proceedings 2025年第1期3159卷

作者： Saraswathi Kuppusamy Renukadevi Thangavel Deepan Kumar Janani Yamunadevi Department of Computer Technology-UG Kongu Engineering College Perundurai Erode Tamilnadu India BSc Software Systems Department of Computer Technology-UG Erode Kongu Engineering College Tamilnadu India

A condition affecting the heart with blood vessels is called Cardio Vascular Disease (CVD). It is the main reason for death. Early detection can help prevent or lessen it, which lowers mortality. Various study articles describe the application of algorithmic machine learning to identify cardiac diseases. When the algorithm is applied to the dataset’s records, a faster and more precise prediction of cardiovascular illnesswill enable the patient to receive therapy. Cardiologists can make judgments quickly with the aid of these projections. The suggested study employs self-defined Decision Tree, random forest, Logistic Regressions, Support Vector Machine (SVM), grid search to identify the presenceof cardiovascular illness. We examine and assess its performance to forecast it.

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Evaluating Hashing Algorithms in the Age of ASIC Resistance 3

Evaluating Hashing Algorithms in the Age of ASIC Resistance

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3rd International Workshop on Information Technologies: Theoretical and Applied Problems, ITTAP 2023

作者： Kuznetsov, Oleksandr Kuznetsova, Yelyzaveta Smirnov, Oleksii Kostenko, Oleksii Zvieriev, Volodymyr Department of Political Sciences Communication and International Relations University of Macerata Via Crescimbeni 30/32 Macerata62100 Italy Department of Information and Communication Systems Security School of Computer Sciences V. N. Karazin Kharkiv National University 4 Svobody Sq. Kharkiv61022 Ukraine Department of cyber security and software Central Ukrainian National Technical University 8 University Ave Kropyvnytskyi25006 Ukraine Scientific Laboratory of the Theory of Digital Transformation Law of the Scientific Center for Digital Transformation Law of the State Scientific Institution "Institute of Information Security Law of the National Academy of Legal Sciences of Ukraine" P.Orlyk str. 3 Kyiv01024 Ukraine Department of Software Engineering and Cybersecurity State University of Trade and Economics Kyoto 19 Kyiv02156 Ukraine

In the intricate matrix of cryptographic hashing, two contrasting paradigms vie for precedence: computational swiftness and resilience against specialized hardware, or ASICs. This study undertakes a meticulous exploration into these dueling priorities, juxtaposing conventional stalwarts like SHA-2 and KECCAK against the newer, ASIC-resistant X series. Leveraging detailed performance metrics and visual analytics, we discern the manifest advantage of SHA-2 and KECCAK in terms of computational alacrity. However, as the paper delves deeper, a compelling narrative unfolds. The deliberate design intricacies of the X series, despite their computational latency, emerge as robust bulwarks against potential centralization threats posed by ASIC miners. These algorithms embody a strategic deceleration, ensuring that the cryptocurrency terrain remains hospitable to a broad diaspora of miners. Through this multifaceted lens, we argue that in the cryptographic domain, raw speed is but one facet of a larger, more nuanced picture. The underlying ethos of decentralization and democratic access must guide our trajectory, even if it comes at the expense of pure efficiency. In essence, this paper endeavors to navigate the delicate balance between rapid computation and the overarching principles of an inclusive digital realm. © 2023 CEUR-WS. All rights reserved.

关键词： Computational efficiency

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Brain-Controlled computer Tasks for Paralyzed Persons: Framework Overview

引用

Procedia computer Science 2024年 237卷 28-35页

作者： Amna Imran Haider Chaudhary Maimoona Tariq Zahra Ali Abdul Ahad Hammad Naqvi Paulo Jorge Coelho Ivan Miguel Pires Knowledge Unit of Science and Technology University of Management and Technology Sialkot Pakistan School of Software Northwestern Polytechnical University Xian Shaanxi 710072 P. R. China Department of Computer Science Istanbul Technical University Turkey Polytechnic of Leiria Leiria Portugal Institute for Systems Engineering and Computers at Coimbra Coimbra Portugal Instituto de Telecomunicações Universidade da Beira Interior Covilhã Portugal

To bridge the digital barrier between those with disabilities and those without impairments, several applications can be operated by brain signals. It highlights the importance of employing brain-computer interfaces (BCI) and mentions a specific system called the "Brain-Controlled computer System" that can control a computer cursor and perform mouse actions using brain signals.. The system can control the cursor and provide mouse left/right click with brain signals using a low-cost Brainlink Lite device. Based on this system, we employed machine learning and deep learning methodologies to develop a system that classifies brain signals for cursor movement. The methodology involved gathering user input through surveys, interviews, and comparison studies, as well as utilizing pre-developed datasets collected using a mind sensor. We initially trained a model on a previous dataset, achieving approximately 70% accuracy. However, when applied to the new dataset, the accuracy dropped to 49%. To improve the results, we experimented with more complex models, including additional layers and a dropout layer to prevent overfitting. Despite these enhancements, the accuracy only reached a maximum of 52%. The research team collected a dataset tailored to their specific application. They made a groundbreaking discovery by applying a convolutional neural network (CNN), typically used for image analysis, to process continuous brainwave data. Despite this non-standard use, their hypothesis proved correct, with the CNN model achieving an impressive 80% accuracy in classifying brain signals for controlling a cursor.

关键词： Brain-Controlled Brainlink Lite EEG Machine Learning Deep learning

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Scenario-Assisted Deep Reinforcement Learning

arXiv

引用

arXiv 2022年

作者： Yerushalmi, Raz Amir, Guy Elyasaf, Achiya Harel, David Katz, Guy Marron, Assaf Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot76100 Israel School of Computer Science and Engineering The Hebrew University of Jerusalem Givat Ram Jerusalem91904 Israel Software and Information Systems Engineering Ben-Gurion University of the Negev Beer-Sheva8410501 Israel

Deep reinforcement learning has proven remarkably useful in training agents from unstructured data. However, the opacity of the produced agents makes it difficult to ensure that they adhere to various requirements posed by human engineers. In this work-in-progress report, we propose a technique for enhancing the reinforcement learning training process (specifically, its reward calculation), in a way that allows human engineers to directly contribute their expert knowledge, making the agent under training more likely to comply with various relevant constraints. Moreover, our proposed approach allows formulating these constraints using advanced model engineering techniques, such as scenario-based modeling. This mix of black-box learning-based tools with classical modeling approaches could produce systems that are effective and efficient, but are also more transparent and maintainable. We evaluated our technique using a case-study from the domain of internet congestion control, obtaining promising results. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

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Fundus Image Translation with Scale-Aware Registration and Gradient-Guided GAN

SSRN

引用

SSRN 2024年

作者： Kim, JuChan Le, Duc-Tai Bum, Junghyun Son, Chang-Hwan Lee, Eun Jung Han, Jong Chul Choo, Hyunseung Department of Artificial Intelligence Systems Engineering Sungkyunkwan University Korea Republic of College of Computing and Informatics Sungkyunkwan University Korea Republic of Sungkyun AI Research Institute Sungkyunkwan University Korea Republic of Software Science and Engineering Major Kunsan National University Korea Republic of Department of Ophthalmology Samsung Medical Center Korea Republic of School of Medicine Sungkyunkwan University Korea Republic of Department of Medical Device Management and Research SAIHST Sungkyunkwan University Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

Two major fundus imaging types, conventional fundus (CF) imaging and ultrawidefield fundus (UF) imaging, are widely used in ophthalmology. In a typical clinical setting, both imaging methods are used simultaneously to diagnose diseases depending on the case. This study introduced an enhanced method of modality transformation to boost the diagnostic support capabilities of deep-learning models in ophthalmology. The method proposed in this study to translate UF images (UFIs) to CF images (CFIs) can replace the common clinical approach of both imaging methods, thereby reducing financial and temporal burdens on patients. This study learns and transforms the relationship between these modalities using UFI–CFI image pairs obtained from the same patient on the same day to minimize distorted information and accurately convert between different modalities. Our proposed model uses scale-aware registration and gradient-guided translation methods to align the UFI–CFI pairs, The generated CFIs not only enhance the quality of images and better represent the retinal area compared to existing methods but also effectively preserve the disease-related details in UFIs, aiding in the diagnosis. In addition, we optimize the image transformation performance, resulting in an 22% improvement in image quality assessment scales compared with previous methods. Furthermore, we acquired the observation that our results are nearly impossible to discern any differences between the real CFIs and the generated CFIs in the view of ophthalmologists. © 2024, The Authors. All rights reserved.

关键词： Ophthalmology

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