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9th International Conference on Cloud Computing and Internet of Things, CCIOT 2024

作者： Lee, Jaden Jinu Nava, Judah J. Lee, Hanku Computer Science & Information Systems Department Minnesota State University Moorhead MoorheadMN United States

ISBN: (纸本)9798400717161

Python's popularity as an interpreted language, particularly among scientists and engineers, is due to its ease of use and flexibility, despite certain performance limitations. Enhancing Python for parallel programming opens significant opportunities in both parallel and cloud computing environments. mpiPython, a Python library designed for message-passing, enables Single Program Multiple Data (SPMD) execution and supports efficient parallel processing. One of its key advantages is its simplicity - both in installation and use - making it accessible to a wide range of users without compromising performance. This paper introduces an extended version of mpiPython, which addresses shortcomings in collective operations for parallel computing while maintaining the library's strengths: ease of installation and minimal overhead. mpiPython is designed with a direct implementation in C, avoiding additional layers, unlike mpi4py, allowing for more efficient communication without unnecessary overhead. The latest version introduces key enhancements to collective communication, including MPI All-Gather, MPI-Send, and MPI-Receive functions. These additions significantly improve performance, particularly in large-scale distributed systems, while preserving mpiPython's focus on being lightweight and user-friendly. Our results show that mpiPython offers competitive performance compared to other MPI libraries while remaining accessible to users who prioritize simplicity and efficiency. The latest fully functional release of mpiPython is now officially available, providing a streamlined solution for Python-based parallel computing. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Python

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Supervisory Control of Discrete Event systems Modeled With Labeled Petri Nets for Diagnosability Enforcement

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IEEE Transactions on Automation Science and Engineering 2025年 22卷 13855-13870页

作者： Li, Chengzong Chen, Yufeng Mostafa, Almetwally M. Khalgui, Mohamed Qinghai University School of Energy and Electrical Engineering Xining810000 China Macau University of Science and Technology Institute of Systems Engineering Taipa China King Saud University College of Computer and Information Sciences Department of Information Systems Riyadh11543 Saudi Arabia German International University Faculty of Engineering Berlin13507 Germany

This paper proposes an active fault diagnosis method to enforce the diagnosability of discrete event systems using labeled Petri nets by constructing a diagnostic supervisor. For a non-diagnosable net model, its diagnosability is viewed as a control specification and addressed by using supervisory control techniques. First, an event-based monitor and a Petri net structure, referred to as data isolation arcs, are introduced to apply control specifications for labeled Petri nets. Then, a diagnostic supervisor reachability graph is generated to estimate the current state of the diagnoser. By analyzing the diagnostic supervisor reachability graph, an integer linear programming model is formulated to design a diagnostic supervisor, which can prevent the system from entering into any indeterminate cycle only by observing the occurrence of events. Finally, by the obtained diagnostic supervisor, the resulting net model is shown to be diagnosable. Some examples are presented to demonstrate the proposed method. © 2004-2012 IEEE.

关键词： Supervisory personnel

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A review on automated plant disease detection: motivation, limitations, challenges, and recent advancements for future research

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Journal of King Saud University - computer and information Sciences 2025年第3期37卷 1-36页

作者： Khan, Sajid Ullah Alsuhaibani, Anas Alabduljabbar, Abdulrahman Almarshad, Fahdah Altherwy, Youssef N. Akram, Tallha Department of Information Systems College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia

Developing countries face significant challenges in healthcare, education, technological advancement, and farming, with agriculture playing an important role in economic growth. Ensuring adequate food production is essential for citizens' survival and economic stability. Early detection of plant disease is critical for increasing agricultural yields through efficient control methods. As a result, autonomous plant disease detection models are vital for reducing labor-intensive tasks. Machine learning and deep learning models have recently been introduced to automate disease identification in plants by detecting symptoms on their leaves. This study reviews various publications that use machine learning and deep learning approaches to detect plant and crop diseases. This review provides (i) an automated plant disease detection system, (ii) an automated wheat leaf rust detection system, (iii) publicly available plant disease datasets, (iv) an overview of machine learning and deep learning methods for plant disease detection, (v) remote sensing technology in plant disease detection, and (vi) future trends and research directions. This review is contributing to the literature by establishing a solid foundation for the development of more valuable machine learning and deep learning methods for plant disease detection. This review study critically examines various problems, including model generalization, dataset diversity, and computing factors, to assist prospective researchers in developing robust and scalable solutions. Additionally, this review presented a detailed overview of the limitations, challenges, and recent advancements in plant disease detection, especially in wheat leaf rust detection and remote sensing technology. Furthermore, current challenges and future directions in wheat disease detection are thoroughly discussed. This study provides a fundamental guide for the development of AI-driven algorithms, providing strategies to enhance production, profitabili

关键词： Deep learning Leaf rust Machine learning Plant datasets Plant disease Remote Sensing

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Guardian-AI: A novel deep learning based deepfake detection model in images

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Alexandria Engineering Journal 2025年 126卷 507-514页

作者： Alsolai, Hadeel Mahmood, Khalid Alshuhail, Asma Ben Miled, Achraf Alqahtani, Mohammed Alshareef, Abdulrhman Alallah, Fouad Shoie Alghamdi, Bandar M. Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University PO Box 84428 Riyadh11671 Saudi Arabia Department of Information Systems Applied College at Mahayil King Khalid University Saudi Arabia Department of Information Systems College of Computer Sciences & Information Technology King Faisal University Saudi Arabia Department of Computer Science College of Science Northern Border University Arar73213 Saudi Arabia Department of Information System and Cyber Security College of Computing and Information Technology University of Bisha Bisha61922 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

The rapid advancement of deepfake technology has introduced significant challenges and opportunities across various domains. This article proposes a robust deepfake detection pipeline utilising a combination of attention mechanisms, pre-trained Vision Transformers (ViTs), and Long Short-Term Memory (LSTM) networks. The initial phase of the pipeline involves preparing photos and videos, potentially using optional facial detection to enhance accuracy. Vision Transformers derive features by capturing the global dependencies of input data. Long short-term memory (LSTM) networks address inter-frame temporal dependencies, whereas multi-head and traditional attention processes focus on essential elements. Ultimately, fully connected layers are employed for classification within the ensemble architecture, which consolidates the outcomes of several models. To ensure generalisability, assessment and regularisation approaches are employed to train the model using labelled datasets. Given the escalating threat of deepfakes, the findings indicate that the pipeline can consistently distinguish between genuine and fabricated information. © 2025 The Authors

关键词： Deep learning

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Detecting Advanced Persistent Threat Exfiltration with Ensemble Deep Learning Tree Models and Novel Detection Metrics

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IEEE Access 2025年 13卷 81803-81822页

作者： Cai, Xiaojuan Zhang, Haibo Ahmed, Chuadhry Mujeeb Koide, Hiroshi Kyushu University Faculty of Information Science and Electrical Engineering Department of Information Science and Technology Fukuoka819-0395 Japan Kyushu Institute of Technology Faculty of Computer Science and Systems Engineering Department of Artificial Intelligence Fukuoka Iizuka820-8502 Japan Newcastle University School of Computing Secure and Resilient Systems Group Newcastle upon TyneNE1 7RU United Kingdom Kyushu University Section of Cyber Security for Information Systems Research Institute for Information Technology Fukuoka819-0395 Japan

Advanced Persistent Threats (APTs) involve attackers maintaining a long-term presence on victim systems, leading to the stealthy exfiltration of sensitive data during network transfers. Despite existing methods to detect and halt APT data exfiltration, these attacks continue to pose significant threats to sensitive information and result in substantial commercial losses. Current approaches primarily focus on preemptive measures, which are insufficient once early-stage detection fails due to a lack of continuous monitoring. We propose an effective and efficient network monitoring method to address this gap and detect APT exfiltration during data transfer. Our approach assumes the presence of an undetected APT attacker within the victim system. We examine data exfiltration across three exfiltration traffic environments: exfiltration over command control channels, exfiltration over transfer size limitations, and their combinations. We introduce two detection metrics: Package Transfer Rate and Byte Transfer Rate. Utilizing these metrics, we measure network traffic, categorize APT attack environments, and train deep neural network models, named EDXGB, using ensembled decision trees to predict APT exfiltration. Our method is validated on two public datasets and compared against six baseline methods. Additionally, we simulate real-world exfiltration scenarios by creating three exfiltration traffic environments for each dataset. The results demonstrate that our method effectively detects APT exfiltration across various network environments, enhancing data protection and secure transfer. The code is open source and available at https://***/cxjuan/EDXGB-for-APT. © 2013 IEEE.

关键词： Sensitive data

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A Secure IoT Network for Healthcare systems Based on Quantum Key Distribution Protocol and AES Encryption 16th

A Secure IoT Network for Healthcare Systems Based on Quantum...

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16th International Conference on Genetic and Evolutionary Computing, ICGEC 2024

作者： Win, Myat Su Wai, Khaing Khaing Faculty of Computer Systems and Technologies University of Computer Studies Mandalay Myanmar Department of Information Technology Support and Maintenance University of Computer Studies Yangon Myanmar

ISBN: (纸本)9789819615308

The primary function of communication through the Internet is to ensure data security. Internet of Things (IoT) devices have evolved to include embedded systems and sensors capable of connecting, gathering, and sending information to the Internet. The main goal is to secure the entire data path: sensor-to-gateway and gateway-to-server data communication. In conventional cryptosystems, the strength of the encryption key is crucial in preventing unauthorized access to sensitive data. However, its security is limited as discovering a quick way to determine the secret key can lead to a system security breach. Quantum cryptography is a unique type that utilizes a quantum characteristic in transmitting information known as photons to guarantee absolute security for the data being sent. This paper tested three Quantum Key Distribution protocols (BB84, B92, and BBM92) to determine which protocol suits IoT network security using the QuVis simulator. As a result, the B92 protocol is better than the other two protocols. This research proposes a solution to secure an IoT network using Quantum Key Distribution Protocol B92 and the Advanced Encryption Standard (AES). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sensor nodes

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Filipino Benchmarks for Measuring Sexist and Homophobic Bias in Multilingual Language Models from Southeast Asia 1

Filipino Benchmarks for Measuring Sexist and Homophobic Bias...

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1st Workshop on Language Models for Low-Resource Languages, LoResLM 2025 - co-located at the 31st International Conference on Computational Linguistics, COLING 2025

作者： Gamboa, Lance Calvin Lee, Mark School of Computer Science University of Birmingham United Kingdom Department of Information Systems and Computer Science Ateneo de Manila University Philippines

ISBN: (纸本)9798891762152

Bias studies on multilingual models confirm the presence of gender-related stereotypes in masked models processing languages with high NLP resources. We expand on this line of research by introducing Filipino CrowS-Pairs and Filipino WinoQueer: benchmarks that assess both sexist and anti-queer biases in pretrained language models (PLMs) handling texts in Filipino, a low-resource language from the Philippines. The benchmarks consist of 7, 074 new challenge pairs resulting from our cultural adaptation of English bias evaluation datasets—a process that we document in detail to guide similar forthcoming efforts. We apply the Filipino benchmarks on masked and causal multilingual models, including those pretrained on Southeast Asian data, and find that they contain considerable amounts of bias. We also find that for multilingual models, the extent of bias learned for a particular language is influenced by how much pretraining data in that language a model was exposed to. Our benchmarks and insights can serve as a foundation for future work analyzing and mitigating bias in multilingual models. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Integrating computer science and information systems 99

Integrating computer science and information systems

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Working group reports from ITiCSE on Innovation and technology in computer science education

作者： Douglas B. Bock Robert W. Klepper John F. Schrage G. Gregory Stephen Bernard Waxman Department of Computer Management and Information Systems School of Business Department of Computer Science School of Engineering

ISBN: (纸本)9781450373258

The Master of Science, major in Computing and information systems (CIS) is a graduate program offered jointly by the graduate faculties of the Department of computer Science in the School of Engineering and the Department of computer Management and information systems in the School of Business. We describe the nature of the program and emphasizes the advantages of a joint degree program that spans academic units. We also touch on problems that may be encountered and how they can be overcome. The successes realized and situations encountered by this merger may benefit other institutions that face similar resource constraints.

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Exploring a Mixer-Based Model for Enhanced Loop Closure Detection in Robotic Navigation 24

Exploring a Mixer-Based Model for Enhanced Loop Closure Dete...

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7th International Conference on Computational Intelligence and Intelligent systems, CIIS 2024

作者： Go, Kendra Kirsten L. Alampay, Raphael B. Department of Information Systems and Computer Science Ateneo de Manila University Metro Manila Quezon City Philippines

ISBN: (纸本)9798400717437

Mixers offer a strong balance between resource efficiency and accuracy which makes them promising feature extractors for loop closure detection (LCD). This paper evaluates the performance of Mixers in LCD by comparing them against handcrafted and learned feature extractors. Using a Siamese architecture with dynamic margin distance, Mixers effectively distinguish loops from non-loops. In most of the chosen datasets, the proposed model achieved higher MR scores than that of the other models while requiring fewer resources. These results demonstrate that the Mixer-based approach is both effective and resource-efficient compared to current methods. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Mixers (machinery)

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A novel U-net model for brain tumor segmentation from MRI images

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Alexandria Engineering Journal 2025年 126卷 220-230页

作者： Obayya, Marwa Alshuhail, Asma Mahmood, Khalid Alanazi, Meshari H. Alqahtani, Mohammed Aljehane, Nojood O. Almansour, Hamad Al-Hagery, Mohammed Abdullah Department of Biomedical Engineering College of Engineering Princess Nourah bint Abdulrahman University PO Box 84428 Riyadh11671 Saudi Arabia Department of Information Systems College of Computer Sciences & Information Technology King Faisal university Saudi Arabia Department of Information Systems Applied College at Mahayil King Khalid University Saudi Arabia Department of Computer Science College of Sciences Northern Border University Arar Saudi Arabia Department of Information System and Cyber Security College of Computing and Information Technology University of Bisha Bisha61922 Saudi Arabia Department of Computer Science Faculty of Computers and Information Technology University of Tabuk Tabuk Saudi Arabia Applied Collage Najran University Najran Saudi Arabia Department of Computer Science College of Computer Qassim University Saudi Arabia

Segmentation of brain tumors aids in diagnosing the disease early, planning treatment, and monitoring its progression in medical image analysis. Automation is necessary to eliminate the time and variability associated with traditional segmentation methods. Convolutional neural networks (CNNs) and U-Net architectures have demonstrated their efficiency and effectiveness in segmenting brain tumors from MRI images using deep learning techniques. The paper presents an improved U-Net-based segmentation algorithm that integrates nested skip paths to improve encoder-decoder feature fusion. The performance of segmentation was optimized by utilizing a variety of activation functions and loss functions, including Dice Loss and Intersection over Union (IoU). A high level of accuracy was demonstrated in the proposed model when it was evaluated using the LGG Segmentation Dataset. The proposed approach for segmenting medical images has been shown to be both robust and efficient in a comparative analysis. © 2025 The Authors

关键词： Convolutional neural networks

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