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arXiv 2024年

作者： Dong, Zehao Zhao, Qihang Payne, Philip R.O. Province, Michael A. Cruchaga, Carlos Zhang, Muhan Zhao, Tianyu Chen, Yixin Li, Fuhai Department of Computer Science & Engineering Washington University St. Louis United States Washington University School of Medicine St. Louis United States Department of Pediatrics Washington University School of Medicine St. Louis United States Institute for Artificial Intelligence Peking University China Department of Genetics Washington University School of Medicine St. Louis United States Department of Psychiatry Washington University School of Medicine St. Louis United States Department of Radiation Oncology Washington University School of Medicine St. Louis United States Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong

Biomarker identification is critical for precise disease diagnosis and understanding disease pathogenesis in omics data analysis, like using fold change and regression analysis. Graph neural networks (GNNs) have been the dominant deep learning model for analyzing graph-structured data. However, we found two major limitations of existing GNNs in omics data analysis, i.e., limited-prediction/diagnosis accuracy and limited-reproducible biomarker identification capacity across multiple datasets. The root of the challenges is the unique graph structure of biological signaling pathways, which consists of a large number of targets and intensive and complex signaling interactions among these targets. To resolve these two challenges, in this study, we presented a novel GNN model architecture, named PathFormer, which systematically integrate signaling network, priori knowledge and omics data to rank biomarkers and predict disease diagnosis. In the comparison results, PathFormer outperformed existing GNN models significantly in terms of highly accurate prediction capability ( 30% accuracy improvement in disease diagnosis compared with existing GNN models) and high reproducibility of biomarker ranking across different datasets. The improvement was confirmed using two independent Alzheimer's Disease (AD) and cancer transcriptomic datasets. The PathFormer model can be directly applied to other omics data analysis studies. Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting

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Video Forgery Detection for Surveillance Cameras: A Review

arXiv

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arXiv 2025年

作者： Tayfor, Noor B. rashid, Tarik A. Qader, Shko M. Hassan, Bryar A. Abdalla, Mohammed H. Majidpour, Jafar Ahmed, Aram M. Ali, Hussein M. Aladdin, Aso M. Abdullah, Abdulhady A. Shamsaldin, Ahmed S. Sidqi, Haval M. Salih, Abdulrahman Yaseen, Zaher M. Ameen, Azad A. Nayak, Janmenjoy Hamza, Mahmood Yashar Department of Information Technology Kurdistan Technical Institute Sulaimani Iraq Computer Science and Engineering Department University of Kurdistan Hewlêr Erbil Iraq Department of Computer Science Kurdistan Technical Institute Sulaimani Iraq Department of Computer Science University of Raparin Rania Iraq Artificial Intelligence and Innovation Centre University of Kurdistan Hewler Erbil Iraq Department of Software Engineering College of Engineering and Computational Science Charmo University Chamchamal46023 Iraq Department of Database College of Informatics Sulaimani Polytechnic University Sulaimani Iraq Northumbria University London United Kingdom Civil and Environmental Engineering Department King Fahd University of Petroleum and Minerals Dhah-ran31261 Saudi Arabia Department of Computer Science Maharaja Sriram Chandra Bhanja Deo University Odisha Baripada India Department of Computer Engineering Tishk International University Erbil Iraq

The widespread availability of video recording through smartphones and digital devices has made video-based evidence more accessible than ever. Surveillance footage plays a crucial role in security, law enforcement, and judicial processes. However, with the rise of advanced video editing tools, tampering with digital recordings has become increasingly easy, raising concerns about their authenticity. Ensuring the integrity of surveillance videos is essential, as manipulated footage can lead to misinformation and undermine judicial decisions. This paper provides a comprehensive review of existing forensic techniques used to detect video forgery, focusing on their effectiveness in verifying the authenticity of surveillance recordings. Various methods, including compression-based analysis, frame duplication detection, and machine learning-based approaches, are explored. The findings highlight the growing necessity for more robust forensic techniques to counteract evolving forgery methods. Strengthening video forensic capabilities will ensure that surveillance recordings remain credible and admissible as legal evidence. © 2025, CC BY.

关键词： CCD cameras

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Assistive Technical Communication of Montenegrin eServices: a Case Study

Assistive Technical Communication of Montenegrin eServices: ...

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International Scientific-Professional Conference on Information technology (IT)

作者： Aniko Simon Luka Laković Pavle Kovačević Peter A. Kara Ivana Ognjanović ramo Šendelj Christoph Reich Milovan Roganović John Mantas Laszlo Bokor Sigma Technology 1093 Budapest Hungary Faculty for Information Systems and Technologies University of Donja Gorica 81000 Podgorica Montenegro Budapest University of Technology and Economics 1111 Budapest Hungary Department of Networked Systems and Services Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics 1111 Budapest Hungary Department of Computer Science School of Computer Science and Mathematics Faculty of Science Engineering and Computing Wireless Multimedia and Networking Research Group Kingston University London KT1 2EE UK Humanistic Studies University of Donja Gorica 81000 Podgorica Montenegro Institute of Data Science Cloud Computing and IT Security Furtwangen University 78120 Furtwangen Germany Clinic for Neurology Clinical Centre of Montenegro 81000 Podgorica Montenegro Faculty of Nursing School of Health Sciences National and Kapodistrian University of Athens 11527 Goudi Greece

With the advancement of information technology in the recent decades, digital and online services have become essential components of everyday life in Europe. Montenegro is no different in this regard. However, as digital literacy in the country is still in a developing phase in comparison with most countries of the EU, a higher portion of its citizens may rely on assistive technical communication. In this paper, we provide a case study on the assistive technical communication of Montenegrin eServices. The work addresses the sectors of online banking, healthcare services, higher education, national administrative services, and utilities. Our findings highlight the diversity in terms of implemented assistive technical communication, and distinguish usage contexts on the level of adequacy. Yet as the functionalities of Montenegrin eServices are expected to gradually expand in the upcoming years, assistive technical communication must aim to handle the higher extents of service complexity.

关键词： Online banking Education Europe Medical services Complexity theory Information technology

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Federated learning in network traffic classification: Taxonomy of implementation, application, and impact on sixth-generation wireless networks

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engineering Applications of Artificial Intelligence 2025年 158卷

作者： Azizi Ariffin Firdaus Afifi Faiz Zaki Hazim Hanif Nor Badrul Anuar Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya 50603 Kuala Lumpur Malaysia School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA 40450 Shah Alam Selangor Malaysia Faculty of Computer Science and Mathematics Universiti Malaysia Terengganu Kuala Nerus Terengganu 21030 Malaysia Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Malaya 50603 Kuala Lumpur Malaysia Centre of Research for Cyber Security and Network (CSNET) Faculty of Computer Science and Information Technology Universiti Malaya 50603 Kuala Lumpur Malaysia Institute of Informatics & Computing in Energy Universiti Tenaga Nasional Putrajaya Kampus 43000 Kajang Selangor Malaysia

Objectives Network traffic classification (NTC) is crucial for network management. However, the surge in Internet traffic poses scalability and privacy issues. Researchers are turning to federated learning (FL) to tackle these issues. Despite the importance of FL, there is lack of literature that comprehensively reviews its implementation and application within NTC and its impact on Sixth-Generation Wireless (6G) networks. Current surveys cover the technical aspects of NTC without fully addressing the integration of FL and its broader implications. This study aims to review the technical implementation of FL in NTC and its application to various network-related areas, including 6G. Methods This study presents a taxonomy for FL implementation in NTC, considering aspects such as learning and aggregation approaches, topology, and client operations. It examines the limitations of these elements and their effects on performance, efficiency, scalability, and their impact on 6G. This study outlines a taxonomy for FL applications, focusing on privacy preservation, scalable classification, and shared security intelligence. Novelty The proposed taxonomy provides insights into research landscape and highlights its limitations. The analysis of the impact of FL-based NTC on 6G provides insight into its integration and implementation challenges. This study discusses open issues and advocates for future research directions in FL for NTC, including 6G. Findings The study identifies areas needing improvement such as privacy, addressing security, single-point-of-failure, hardware limitations, delays and heterogeneity concerns. The findings of this paper show that an optimal implementation approach is essential to cater for heterogeneity and real-time requirements of the network environment.

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The Dynamics and Control of the Fractional Forms of Some rational Chaotic Maps

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Journal of Systems Science & Complexity 2020年第3期33卷 584-603页

作者： OUANNAS Adel KHENNAOUI Amina-Aicha BENDOUKHA Samir WANG Zhen PHAM Viet-Thanh Laboratory of Mathematics Informatics and Systems(LAMIS)University of Larbi TebessiTebessa 12002Algeria Department of Mathematics and Computer Sciences University of Larbi Ben M'hidiOum El BouaghiAlgeria Department of Electrical Engineering College of EngineeringYanbuTaibah UniversitySaudi Arabia Shaanxi Engineering Research Center of Controllable Neutron Source School of ScienceXijing UniversityXi'an 710123China Faculty of Electrical and Electronic Engineering Phenikaa Institute for Advanced Study(PIAS)Phenikaa UniversityYen NghiaHa Dong districtHanoi 100000Vietnam Phenikaa Research and Technology Institute(PRATI) A&A Green Phoenix Group167 Hoang NganHanoi 100000Vietnam

This paper proposes three fractional discrete chaotic systems based on the Rulkov,Chang,and Zeraoulia–Sprott rational *** dynamics of the proposed maps are investigated by means of phase plots and bifurcations *** stabilization schemes are proposed for each of the three maps and the convergence of the states is established by using the Lyapunov ***,a combination synchronization scheme is proposed whereby a combination of the fractional Rulkov and Chang maps is synchronized to the fractional Zeraoulia-Sprott *** results are used to confirm the findings of the paper.

关键词： Fractional discrete chaos fractional rational maps stabilization synchronization

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Fusion of Super-Resolution and Semantic Segmentation Deep Models for Building Footprint Extraction From Aerial Images

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The Photogrammetric Record 2025年第190期40卷

作者： Ziyi Chen Yuhua Luo Jiaying Zhang Ronggang Guo Liai Deng Jinghua Liu Dilong Li Yongtao Yu Ammar Abulibdeh Cheng Wang Department of Computer Science and Technology Fujian Key Laboratory of big Data Intelligence and Security Xiamen Key Laboratory of Computer Vision and Pattern Recognition Huaqiao University Xiamen FJ China Guojiao Spatial Information Technology (Beijing) co. Ltd Beijing China Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huaian JS China Department of Humanities College of Arts and Sciences Qatar University Doha Qatar School of Informatics Xiamen University Xiamen FJ China

The spatial resolution of remotely sensed images has seen significant improvements; higher resolution facilitates the understanding of remote sensing images and improves the accuracy of building footprint extraction. Thus, this paper introduces a novel approach that combines super-resolution reconstruction with semantic segmentation deep models for building footprint extraction. To begin, a super-resolution reconstruction deep model is employed to enhance the resolution of the target images. Subsequently, training samples are generated using various data augmentation techniques based on the reconstructed images. Finally, a semantic segmentation deep model is trained to perform the final building footprint extraction. For validation, our proposed framework was tested on two publicly available aerial image datasets: the WHU building dataset and the Massachusetts building dataset. The results obtained from these datasets demonstrated the effectiveness of our method, achieving high Intersection over Union (IoU) scores of 90.57% and 75.02%, respectively. Furthermore, our approach outperformed seven other competing methods, showcasing its superior performance.

关键词： aerial image building footprint extraction deep learning semantic segmentation super-resolution

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Resilient intrusion detection system for cloud containers

Resilient intrusion detection system for cloud containers

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作者： Abed, Amr S. Azab, Mohamed Clancy, Charles Kashkoush, Mona S. Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA United States Informatics Research Institute City of Scientific Research and Technological Applications Alexandria Egypt Hume Center for National Security and Technology Virginia Tech ArlingtonVA United States

The lightweight virtualisation and isolated execution offered by Linux containers qualify it to be the dominant virtualisation platform for cloud-based applications. The fact that Linux containers run on the same host while sharing the same kernel opens the door for new attacks. However, limited research has been conducted in the area of securing cloud containers. This paper presents a resilient intrusion detection and resolution system for cloud-based containers. The system relies on two main pillars, a real-time smart behaviour monitoring mechanism to detect maliciously behaving containers, and a moving-target defence approach that applies runtime container migration to quarantine such containers and to minimise attack dispersion. To avoid zero-day targeted attacks, the system also induces random live migrations between running containers to obfuscate its execution behaviour. Such obfuscation makes it harder for attackers to execute their targeted attacks. The system was tested by a big-data application using a container-based Apache Hadoop cluster to demonstrate the system's ability to automatically deploy, monitor, detect, and respond to maliciously behaving applications by live migration or by rolling back the container to a safe state. Results showed that the proposed system efficiently ensure safe and secure container operation. © 2020 Inderscience Enterprises Ltd.

关键词： Containers

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BabyNet＋＋: Fetal birth weight prediction using biometry multimodal data acquired less than 24 hours before delivery

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computers in Biology and Medicine 2023年 167卷 107602-107602页

作者： Plotka, Szymon Grzeszczyk, Michal K. Brawura-Biskupski-Samaha, Robert Gutaj, Pawel Lipa, Michal Trzciński, Tomasz Išgum, Ivana Sánchez, Clara I. Sitek, Arkadiusz Sano Centre for Computational Medicine Cracow Poland Informatics Institute University of Amsterdam Amsterdam Netherlands Department of Biomedical Engineering and Physics Amsterdam University Medical Center Amsterdam Netherlands Second Department of Obstetrics and Gynecology The Medical Centre of Postgraduate Education Warsaw Poland Department of Reproduction Poznan University of Medical Sciences Poznan Poznan Poland First Department of Obstetrics and Gynecology Medical University of Warsaw Warsaw Poland Institute of Computer Science Warsaw University of Technology Warsaw Poland Department of Radiology and Nuclear Medicine Amsterdam University Medical Center location University of Amsterdam Amsterdam Netherlands Center for Advanced Medical Computing and Simulation Massachusetts General Hospital Harvard Medical School BostonMA United States

Accurate prediction of fetal weight at birth is essential for effective perinatal care, particularly in the context of antenatal management, which involves determining the timing and mode of delivery. The current standard of care involves performing a prenatal ultrasound 24 hours prior to delivery. However, this task presents challenges as it requires acquiring high-quality images, which becomes difficult during advanced pregnancy due to the lack of amniotic fluid. In this paper, we present a novel method that automatically predicts fetal birth weight by using fetal ultrasound video scans and clinical data. Our proposed method is based on a Transformer-based approach that combines a Residual Transformer Module with a Dynamic Affine Feature Map Transform. This method leverages tabular clinical data to evaluate 2D+t spatio-temporal features in fetal ultrasound video scans. Development and evaluation were carried out on a clinical set comprising 582 2D fetal ultrasound videos and clinical records of pregnancies from 194 patients performed less than 24 hours before delivery. Our results show that our method outperforms several state-of-the-art automatic methods and estimates fetal birth weight with an accuracy comparable to human experts. Hence, automatic measurements obtained by our method can reduce the risk of errors inherent in manual measurements. Observer studies suggest that our approach may be used as an aid for less experienced clinicians to predict fetal birth weight before delivery, optimizing perinatal care regardless of the available expertise. © 2023 The Author(s)

关键词： Forecasting

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Cyber-Physical System for Industrial Automation Using Quantum Deep Learning

Cyber-Physical System for Industrial Automation Using Quantu...

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International Conference on System Modeling & Advancement in Research Trends (SMART)

作者： Anand Singh rajawat S.B. Goyal Pradeep Bedi Neagu Bogdan Constantin Maria Simona raboaca Chaman Verma School of Computer Sciences and Engineering Sandip University Nashik India Faculty of Information Technology City University Petaling Jaya Malaysia Galgotais University Greater Noida India Department of Power Engineering Gheorghe Asachi Technical University of Iasi Iasi Romania ICSI Energy Department National Research and Development Institute for Cryogenics and Isotopic Technologies Ramnicu Valcea Romania Faculty of Informatics University of Eotvos Lorand Budapest Hungary

ISBN: (纸本)9781665487351

Automation industries are using Industry 4.0 solutions for several reasons. Still, the most important is to make it easier for their employees to get secure information and to work better with people in other departments. The goal of this effort is to improve productivity and efficiency by making it easier to make good decisions at the right time. This will make it possible to make these kinds of improvements. Constraint-based security solutions are becoming increasingly popular as the digital and physical worlds become more connected. Based on the processing speed, the amount of memory that can be used, and the amount of backup power that can be used. The algorithms and security frameworks used in earlier research need many resources. Several researchers have given an overview of the many security problems with the CPS and their studies and recommendations. But constraint-based applications need light security systems that can work with hardware with limited capabilities. We proposed a safety mechanism that can be used with the already-made constraint-based CPS program. To propose Cyber-Physical System for Industrial Automation Using Quantum Deep Learning, We proposed Cyber-Physical System for Industrial Automation that uses Quantum Learning to find attacking patterns.

关键词： Deep learning Productivity Memory management Cyber-physical systems Market research Fourth Industrial Revolution Safety

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AI-based Fog and Edge Computing: A Systematic Review, Taxonomy and Future Directions

arXiv

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arXiv 2022年

作者： Iftikhar, Sundas Gill, Sukhpal Singh Song, Chenghao Xu, Minxian Aslanpour, Mohammad Sadegh Toosi, Adel N. Du, Junhui Wu, Huaming Ghosh, Shreya Chowdhury, Deepraj Golec, Muhammed Kumar, Mohit Abdelmoniem, Ahmed M. Cuadrado, Felix Varghese, Blesson rana, Omer Dustdar, Schahram Uhlig, Steve School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom University of Kotli Azad Jammu & Kashmir Azad Kashmir Kotli Pakistan Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Software Systems and Cybersecurity Faculty of Information Technology Monash University Australia Csiro DATA61 Australia Center for Applied Mathematics Tianjin University Tianjin China The Pennsylvania State University PA United States Naya Raipur India Abdullah Gül University Kayseri Turkey Department of Information Technology National Institute of Technology Jalandhar India Spain School of Computer Science University of St Andrews United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria

Resource management in computing is a very challenging problem that involves making sequential decisions. Resource limitations, resource heterogeneity, dynamic and diverse nature of workload, and the unpredictability of fog/edge computing environments have made resource management even more challenging to be considered in the fog landscape. Recently Artificial Intelligence (AI) and Machine Learning (ML) based solutions are adopted to solve this problem. AI/ML methods with the capability to make sequential decisions like reinforcement learning seem most promising for these type of problems. But these algorithms come with their own challenges such as high variance, explainability, and online training. The continuously changing fog/edge environment dynamics require solutions that learn online, adopting changing computing environment. In this paper, we used standard review methodology to conduct this Systematic Literature Review (SLR) to analyze the role of AI/ML algorithms and the challenges in the applicability of these algorithms for resource management in fog/edge computing environments. Further, various machine learning, deep learning and reinforcement learning techniques for edge AI management have been discussed. Furthermore, we have presented the background and current status of AI/ML-based Fog/Edge Computing. Moreover, a taxonomy of AI/ML-based resource management techniques for fog/edge computing has been proposed and compared the existing techniques based on the proposed taxonomy. Finally, open challenges and promising future research directions have been identified and discussed in the area of AI/ML-based fog/edge computing. Copyright © 2022, The Authors. All rights reserved.

关键词： Edge computing

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