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20th International Learning and Technology Conference, L and T 2023

作者： Siddiqui, Muhammad Shoaib Islamic University of Madinah Faculty of Computer and Information Systems Madinah Saudi Arabia

ISBN: (纸本)9798350300307

Collaborative Mixed Reality (MR) provides a virtual/real world in which various users can interact and build virtual objects together. One of the issues in collaborative MR is permission and rights control that defines which user can access which virtual object. A user may define access rights for other users to access the virtual objects created by her/him, which might enable the other users to manipulate the existing virtual object, delete them, or even steal them. For maintaining accountability in the collaborative world, each activity of each user should be maintained against each object. Hence, an activity provenance mechanism is required for a collaborative MR environment, which can be used to identify the culprit. In this paper, a secure activity provenance mechanism for collaborative MR environments is proposed which utilizes blockchain technologies to maintain the integrity of the provenance data. The proposed scheme is implemented on a consortium blockchain using Hyperledger Fabric as a proof of concept. The presented mechanism is also evaluated using metrics throughput and latency and the results confirm that the proposed solution is best suited for maintaining accountability in a collaborative mixed reality network. © 2023 IEEE.

关键词： Blockchain

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Method for Detecting Steganographic Changes in Images Using Machine Learning 13

Method for Detecting Steganographic Changes in Images Using ...

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13th International Conference on Dependable systems, Services and Technologies, DESSERT 2023

作者： Denysiuk, Dmytro Savenko, Oleg Lysenko, Sergii Savenko, Bohdan Kashtalian, Antonina Khmelnytskyi national university Department of Computer Engineering and Information Systems Khmelnytskyi Ukraine

ISBN: (纸本)9798350396119

This article presents a method for detecting steganographic changes in images using convolutional neural networks. The use of convolutional neural networks made it possible to automatically detect characteristic features and patterns in images, which ensured the high efficiency of the method. Dividing the image into small segments made it possible to search for changes with limited use of computing resources, in particular for the analysis of images on web resources, which can become part of a firewall to prevent steganographic changes. Experiments confirmed the high efficiency of the method in detecting steganographic changes, in particular when modifying images using various steganography methods. It is also found that the detection efficiency depends on the choice of database for training convolutional neural networks. © 2023 IEEE.

关键词： Image analysis

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HPPython: Extending Python with HPspmd for Data Parallel Programming 2

HPPython: Extending Python with HPspmd for Data Parallel Pro...

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2nd International Symposium on Computing and Artificial Intelligence, ISCAI 2023

作者： Nava, Judah Josiah Lee, Jaden Jinu Lee, Hanku Computer Science and Information Systems Department University Moorhead MN United States

ISBN: (纸本)9798400708954

In light of previous endeavors and trends in the realm of parallel programming, HPPython emerges as an essential superset that enhances the accessibility of parallel programming for developers, facilitating scalability across multiple nodes. Despite Python's popularity as a programming language in scientific and engineering applications and its native support for executing various processes, HPPython brings substantial simplification to the development of parallel programs and empowers program distribution across heterogeneous clusters consisting of multiple physical computers. HPPython leverages the MPI standard for its underlying communication, thereby harnessing the benefits of the SPMD model. Additionally, HPPython introduces novel syntax and constructs, such as parallel loops and distributed lists, while endeavoring to retain the natural essence of the original language. This paper delves into the distinct components of HPPython and elucidates their integration, establishing HPPython as a viable solution for parallel programming in today's data-driven world. © 2023 ACM.

关键词： Parallel programming

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Mutual information oriented deep skill chaining for multi‐agent reinforcement learning

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 1014-1030页

作者： Zaipeng Xie Cheng Ji Chentai Qiao WenZhan Song Zewen Li Yufeng Zhang Yujing Zhang Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai UniversityNanjingChina College of Computer and Information Hohai UniversityNanjingChina Center for Cyber‐Physical Systems University of GeorgiaAthensGeorgiaUSA Information Networking Institute Carnegie Mellon UniversityPittsburghPennsylvaniaUSA Department of Electrical and Systems Engineering University of PennsylvaniaPhiladelphiaPennsylvaniaUSA

Multi‐agent reinforcement learning relies on reward signals to guide the policy networks of individual ***,in high‐dimensional continuous spaces,the non‐stationary environment can provide outdated experiences that hinder convergence,resulting in ineffective training performance for multi‐agent *** tackle this issue,a novel reinforcement learning scheme,Mutual information Oriented Deep Skill Chaining(MioDSC),is proposed that generates an optimised cooperative policy by incorporating intrinsic rewards based on mutual information to improve exploration *** rewards encourage agents to diversify their learning process by engaging in actions that increase the mutual information between their actions and the environment *** addition,MioDSC can generate cooperative policies using the options framework,allowing agents to learn and reuse complex action sequences and accelerating the convergence speed of multi‐agent *** was evaluated in the multi‐agent particle environment and the StarCraft multi‐agent challenge at varying difficulty *** experimental results demonstrate that MioDSC outperforms state‐of‐the‐art methods and is robust across various multi‐agent system tasks with high stability.

关键词： artificial intelligence techniques decision making intelligent multi‐agent systems

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Novel Feature Extractor Framework in Conjunction with Supervised Three Class-XGBoost Algorithm for Osteosarcoma Detection from Whole Slide Medical Histopathology Images

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computers, Materials & Continua 2025年第2期82卷 3337-3353页

作者： Tanzila Saba Muhammad Mujahid Shaha Al-Otaibi Noor Ayesha Amjad Rehman Khan Artificial Intelligence&Data Analytics Lab College of Computer&Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Center of Excellence in Cyber Security(CYBEX) Prince Sultan UniversityRiyadh11586Saudi Arabia

Osteosarcomas are malignant neoplasms derived from undifferentiated osteogenic mesenchymal cells. It causes severe and permanent damage to human tissue and has a high mortality rate. The condition has the capacity to occur in any bone;however, it often impacts long bones like the arms and legs. Prompt identification and prompt intervention are essential for augmenting patient longevity. However, the intricate composition and erratic placement of osteosarcoma provide difficulties for clinicians in accurately determining the scope of the afflicted area. There is a pressing requirement for developing an algorithm that can automatically detect bone tumors with tremendous accuracy. Therefore, in this study, we proposed a novel feature extractor framework associated with a supervised three-class XGBoost algorithm for the detection of osteosarcoma in whole slide histopathology images. This method allows for quicker and more effective data analysis. The first step involves preprocessing the imbalanced histopathology dataset, followed by augmentation and balancing utilizing two techniques: SMOTE and ADASYN. Next, a unique feature extraction framework is used to extract features, which are then inputted into the supervised three-class XGBoost algorithm for classification into three categories: non-tumor, viable tumor, and non-viable tumor. The experimental findings indicate that the proposed model exhibits superior efficiency, accuracy, and a more lightweight design in comparison to other current models for osteosarcoma detection.

关键词： Medical image processing deep learning healthcare image classification histopathology

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Adaptive Anomaly Detection in Dynamic Graph Networks

Adaptive Anomaly Detection in Dynamic Graph Networks

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2024 International Visualization, Informatics and Technology Conference, IVIT 2024

作者： Mir, Aabid Ahmad Zuhairi, Megat F. Musa, Shahrulniza Namoun, Abdallah Universiti Kuala Lumpur Malaysian Institute of Information Technology Malaysia Islamic University of Madinah Faculty of Computer and Information Systems Saudi Arabia

ISBN: (纸本)9798350354447

Over the past few years, the detection of anomalies in dynamic graph networks has attracted substantial attention worldwide because of its applications in various fields such as cybersecurity, financial fraud detection, and social network analysis. Traditional methods may not be as adaptive to the dynamic nature of such networks, as it requires more efficient and scalable solutions. To mitigate the issue, a Variational Graph Convolutional Network (V-GCN) is proposed. It is an approach that combines variational autoencoders (VAEs) with graph convolutional networks (GCNs) for anomaly detection in dynamic graph data to enhance anomaly detection. The methodology involves developing a V-GCN architecture that combines the capabilities of VAEs and GCNs, applying an adaptive learning strategy to update model parameters in real time, and carrying out experiments using standard datasets. The experimental results indicate that the anomaly detection performance considerably enhanced using the proposed model. It outperforms the baseline methods, where the V-GCN attains an AUC-ROC of 95%, an average precision of 92%, an F1 score of 91%, and an anomaly score of 94%. © 2024 IEEE.

关键词： Graph neural networks

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Remote Sensing Image Encryption Using Optimal Key Generation-Based Chaotic Encryption

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computer systems Science & Engineering 2023年第9期46卷 3209-3223页

作者： Mesfer Al Duhayyim Fatma S.Alrayes Saud S.Alotaibi Sana Alazwari Nasser Allheeib Ayman Yafoz Raed Alsini Amira Sayed A.Aziz Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Information Systems-College of Computer and Information Sciences King Saud UniversityRiyadhSaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt

The Internet of Things(IoT)offers a new era of connectivity,which goes beyond laptops and smart connected devices for connected vehicles,smart homes,smart cities,and connected *** massive quantity of data gathered from numerous IoT devices poses security and privacy concerns for *** the increasing use of multimedia in communications,the content security of remote-sensing images attracted much attention in academia and *** encryption is important for securing remote sensing images in the IoT ***,researchers have introduced plenty of algorithms for encrypting *** study introduces an Improved Sine Cosine Algorithm with Chaotic Encryption based Remote Sensing Image Encryption(ISCACE-RSI)technique in IoT *** proposed model follows a three-stage process,namely pre-processing,encryption,and optimal key *** remote sensing images were preprocessed at the initial stage to enhance the image ***,the ISCACERSI technique exploits the double-layer remote sensing image encryption(DLRSIE)algorithm for encrypting the *** DLRSIE methodology incorporates the design of Chaotic Maps and deoxyribonucleic acid(DNA)Strand Displacement(DNASD)*** chaotic map is employed for generating pseudorandom sequences and implementing routine scrambling and diffusion processes on the plaintext ***,the study presents three DNASD-related encryption rules based on the variety of DNASD,and those rules are applied for encrypting the images at the DNA sequence *** an optimal key generation of the DLRSIE technique,the ISCA is applied with an objective function of the maximization of peak signal to noise ratio(PSNR).To examine the performance of the ISCACE-RSI model,a detailed set of simulations were *** comparative study reported the better performance of the ISCACE-RSI model over other existing approaches.

关键词： Remote sensing internet of things image encryption security optimal key generation

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Enhanced Growth Optimizer and Its Application to Multispectral Image Fusion

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computers, Materials & Continua 2024年第11期81卷 3033-3062页

作者： Jeng-Shyang Pan Wenda Li Shu-Chuan Chu Xiao Sui Junzo Watada College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China Department of Information Management Chaoyang University of TechnologyTaichung413310Taiwan Graduate School of Information Production and SystemsWaseda UniversityKitakyushu808-0135Japan

The growth optimizer(GO)is an innovative and robust metaheuristic optimization algorithm designed to simulate the learning and reflective processes experienced by individuals as they mature within the social ***,the original GO algorithm is constrained by two significant limitations:slow convergence and high mem-ory *** restricts its application to large-scale and complex *** address these problems,this paper proposes an innovative enhanced growth optimizer(eGO).In contrast to conventional population-based optimization algorithms,the eGO algorithm utilizes a probabilistic model,designated as the virtual population,which is capable of accurately replicating the behavior of actual populations while simultaneously reducing memory ***,this paper introduces the Lévy flight mechanism,which enhances the diversity and flexibility of the search process,thus further improving the algorithm’s global search capability and convergence *** verify the effectiveness of the eGO algorithm,a series of experiments were conducted using the CEC2014 and CEC2017 test *** results demonstrate that the eGO algorithm outperforms the original GO algorithm and other compact algorithms regarding memory usage and convergence speed,thus exhibiting powerful optimization ***,the eGO algorithm was applied to image *** a comparative analysis with the existing PSO and GO algorithms and other compact algorithms,the eGO algorithm demonstrates superior performance in image fusion.

关键词： Growth optimizer probabilistic model Lévy flight image fusion

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Building Cybersecurity Mindset Through Continuous Cross-Module Learning 14

Building Cybersecurity Mindset Through Continuous Cross-Modu...

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14th IEEE Integrated STEM Education Conference, ISEC 2024

作者： Yuan, Jiawei Li, Yanyan University of Massachusetts Department of Computer and Information Science Dartmouth United States California State University Department of Computer Science and Information Systems San Marcos United States

ISBN: (纸本)9798350352801

Recent years have witnessed a strong demand for cybersecurity professionals, especially considering the remarkable projected growth rate of cybersecurity positions for the next ten years. Filling such a workforce demand is not simply preparing dedicated cybersecurity students. More importantly, it is essential to establish a cybersecurity mindset for computer science students in general, because they will contribute to the majority of the cybersecurity-related workforce in the near future. This project aims to promote cybersecurity education for undergraduate computer science students in general and prepare a future cybersecurity workforce. Instead of heavily relying on dedicated cybersecurity courses in most existing undergraduate cybersecurity education, this project proposes the concept of cross-module learning that leverages non-cybersecurity computer sciences courses at different levels to continuously instill security concepts and skills to train students from the early stage of undergraduate learning. To enable effective cross-module learning, we identify appropriate computer science courses to integrate cybersecurity modules, investigate effective integration strategies, and explore potential linkages among different courses and modules. © 2024 IEEE.

关键词： Students

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Velocity control of a Stephenson III six-bar linkage-based gait rehabilitation robot using deep reinforcement learning

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Neural Computing and Applications 2025年第7期37卷 5671-5682页

作者： Kapsalyamov, Akim Brown, Nicholas A. T. Goecke, Roland Jamwal, Prashant K. Hussain, Shahid School of Information Technology and Systems University of Canberra CanberraACT2617 Australia School of Clinical Sciences Queensland University of Technology BrisbaneQLD Australia School of Systems and Computing UNSW CanberraACT Australia Department of Electrical and Computer Engineering Nazarbayev University Nur-Sultan City Kazakhstan

Lower limb rehabilitation robots can help to improve the locomotor capabilities of patients experiencing gait impairments and help medical workers by reducing strain on them. However, since commercially available exoskeletons are expensive and there is a lack of number of physiotherapists many patients are still not able to get proper rehabilitation training. The closed-loop linkage mechanisms have recently drawn much attention in the realization of gait rehabilitation robots. Such mechanisms are affordable and capable of providing suitable trajectories for gait training therapy. In this work, we have proposed a fully operational one degree-of-freedom mechanism which can generate complex naturalistic lower limb trajectories. Although in theory, it is assumed that the constant speed applied at the input crank is sufficient to control the system, in reality, the external forces exerted by human legs and the inertia of the links can greatly alter the rotational velocity at the crank, which may negatively affect the training process. Therefore, we have explored the performance of a deep reinforcement learning-based control algorithm designed to regulate the speed of the input crank to reach satisfactory performance needed for gait rehabilitation training. Experimental evaluations with healthy human subjects were conducted to demonstrate that the mechanism is capable of directing lower limbs on naturalistic gait trajectories with a required walking speed. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Exoskeleton (Robotics)

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