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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Maruf, Md Al Azim, Akramul Auluck, Nitin Sahi, Mansi Ontario Tech University Department of Electrical Computer and Software Engineering Oshawa Canada Indian Institute of Technology Departmcnt of Computer Science and Engineering Ropar India

ISBN: (纸本)9798350345346

With the increasing demand for edge computing in cyber-physical system (CPS) applications, ensuring the safety and reliability of machine learning models running on edge devices during online model training and inference is essential. Although data and model parallelism offer significant advantages for large machine learning model training, adopting parallel computing architecture in edge networks is challenging. It introduces safety concerns while splitting and integrating machine learning models over different computing nodes, which can pose risks to the integrity and reliability of the system. Therefore, online model training and inference in edge networks require a safe parallel computing architecture to achieve improved performance with optimal resource utilization. To address this challenge, we propose an efficient machine learning model partitioning algorithm that considers the safety constraint and requirements of edge networks and includes the triple-modular redundancy (TMR) technique for trusted computation. Our proposed approach achieves a significant speedup of approximately 56.3% in net training time compared to the non-partitioning approach, making it more efficient and suitable for real-time applications in edge networks. © 2023 IEEE.

关键词： Edge Networks Machine Learning Model Partitioning Triple Modular Redundancy (TMR)

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IDS-INT:Intrusion detection system using transformer-based transfer learning for imbalanced network traffic

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Digital Communications and Networks 2024年第1期10卷 190-204页

作者： Farhan Ullah Shamsher Ullah Gautam Srivastava Jerry Chun-Wei Lin School of Software Northwestern Polytechnical UniversityXian710072ShaanxiChina Knowledge Unit of Systems and Technology University of Management and TechnologySialkot 51040Pakistan Department of Math and Computer Science Brandon UniversityBrandonMB R7A 6A9Canada Department of Computer Science and Math Lebanese American UniversityBeirut1102Lebanon Western Norway University of Applied Sciences BergenNorway Research Centre for Interneural Computing China Medical UniversityTaichungTaiwanChina

A network intrusion detection system is critical for cyber security against llegitimate *** terms of feature perspectives,network traffic may include a variety of elements such as attack reference,attack type,a subcategory of attack,host information,malicious scripts,*** terms of network perspectives,network traffic may contain an imbalanced number of harmful attacks when compared to normal *** is challenging to identify a specific attack due to complex features and data imbalance *** address these issues,this paper proposes an Intrusion Detection System using transformer-based transfer learning for Imbalanced Network Traffic(IDS-INT).IDS-INT uses transformer-based transfer learning to learn feature interactions in both network feature representation and imbalanced ***,detailed information about each type of attack is gathered from network interaction descriptions,which include network nodes,attack type,reference,host information,***,the transformer-based transfer learning approach is developed to learn detailed feature representation using their semantic ***,the Synthetic Minority Oversampling Technique(SMOTE)is implemented to balance abnormal traffic and detect minority ***,the Convolution Neural Network(CNN)model is designed to extract deep features from the balanced network ***,the hybrid approach of the CNN-Long Short-Term Memory(CNN-LSTM)model is developed to detect different types of attacks from the deep *** experiments are conducted to test the proposed approach using three standard datasets,i.e.,UNsWNB15,CIC-IDS2017,and *** explainable AI approach is implemented to interpret the proposed method and develop a trustable model.

关键词： Network intrusion detection Transfer learning Features extraction Imbalance data Explainable AI Cybersecurity

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Evolutionary Optimization with a Simplified Helper Task for High-Dimensional Expensive Multiobjective Problems

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ACM Transactions on Evolutionary Learning and Optimization 2024年第4期4卷 1-32页

作者： Wu, Xunfeng Lin, Qiuzhen Zhou, Junwei Liu, Songbai Coello Coello, Carlos A. Leung, Victor C. M. College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan430070 China Mexico City07300 Mexico Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada

In recent years, surrogate-Assisted evolutionary algorithms (SAEAs) have been sufficiently studied for tackling computationally expensive multiobjective optimization problems (EMOPs), as they can quickly estimate the qualities of solutions by using surrogate models to substitute for expensive evaluations. However, most existing SAEAs only show promising performance for solving EMOPs with no more than 10 dimensions, and become less efficient for tackling EMOPs with higher dimensionality. Thus, this article proposes a new SAEA with a simplified helper task for tackling high-dimensional EMOPs. In each generation, one simplified task will be generated artificially by using random dimension reduction on the target task (i.e., the target EMOPs). Then, two surrogate models are trained for the helper task and the target task, respectively. Based on the trained surrogate models, evolutionary multitasking optimization is run to solve these two tasks so that the experiences of solving the helper task can be transferred to speed up the convergence of tackling the target task. Moreover, an effective model management strategy is designed to select new promising samples for training the surrogate models. When compared to five competitive SAEAs on four well-known benchmark suites, the experiments validate the advantages of the proposed algorithm on most test cases. © 2024 held by the owner/author(s).

关键词： Multitasking

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Demystifying AI Advances in Explainable AI (XAI) 3

Demystifying AI Advances in Explainable AI (XAI)

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3rd International Conference on Computational Modelling, Simulation and Optimization, ICCMSO 2024

作者： Kumar Pandiya, Dileep Ramrao Joshi, Vilas Tripathi, Kailash Nath ZoomInfo Technology Inc Principal Software Engineer BostonMA United States ISBM College of Engineering Department of Computer Engineering Maharashtra Pune India

ISBN: (纸本)9798350361391

In today's dynamic landscape, the integration of artificial intelligence (AI) has revolutionized operations across diverse domains. However, the enigmatic nature of many AI algorithms presents formidable obstacles, spanning from a dearth of confidence to potential ethical quandaries. Enter Explainable AI (XAI), a burgeoning field that serves as a beacon of clarity, providing to augment transparency and comprehension. This manuscript embarks on a comprehensive exploration of the strides made in XAI, meticulously dissecting its significance, fundamental methodologies, practical applications, hurdles, ethical dimensions, and trajectories for future advancement. By delving into these multifaceted dimensions, this endeavor aims to illuminate the trajectory towards closing the chasm between intricate AI frameworks and human cognition. © 2024 IEEE.

关键词： Advancements Artificial Intelligence Ethical implications Explainable AI (XAI) Transparency

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Federated Machine Learning Based Fetal Health Prediction Empowered with Bio-Signal Cardiotocography

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computers, Materials & Continua 2024年第3期78卷 3303-3321页

作者： Muhammad Umar Nasir Omar Kassem Khalil Karamath Ateeq Bassam SaleemAllah Almogadwy Muhammad Adnan Khan Muhammad Hasnain Azam Khan Muhammad Adnan Department of Computer Sciences Bahria University Lahore CampusLahore54000Pakistan Faculty of Information Technology Liwa CollegeAbu Dhabi00000United Arab Emirates Department of Computing Skyline University CollegeSharjah1797United Arab Emirates Department of Computer Science Taibah UniversityMedina42353Saudi Arabia Riphah School of Computing and Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Engineering and Development Dubizzle LabsLahore54000Pakistan Department of Software Faculty of Artificial Intelligence and SoftwareGachon UniversitySeongnam-si13120Korea

Cardiotocography measures the fetal heart rate in the fetus during pregnancy to ensure physical health because cardiotocography gives data about fetal heart rate and uterine shrinkages which is very beneficial to detect whether the fetus is normal or suspect or *** cardiotocography measures infer wrongly and give wrong predictions because of human *** traditional way of reading the cardiotocography measures is the time taken and belongs to numerous human errors as *** condition is very important to measure at numerous stages and give proper medications to the fetus for its *** the current period Machine learning(ML)is a well-known classification strategy used in the biomedical field on various issues because ML is very fast and gives appropriate results that are better than traditional *** techniques play a pivotal role in detecting fetal disease in its early *** research article uses Federated machine learning(FML)and ML techniques to classify the condition of the *** study proposed a model for the detection of bio-signal cardiotocography that uses FML and ML techniques to train and test the ***,the proposed model of FML used numerous data preprocessing techniques to overcome data deficiency and achieves 99.06%and 0.94%of prediction accuracy and misprediction rate,respectively,and parallel the proposed model applying K-nearest neighbor(KNN)and achieves 82.93%and 17.07%of prediction accuracy and misprediction accuracy,***,by comparing both models FML outperformed the KNN technique and achieved the best and most appropriate prediction results as compared with previous studies the proposed study achieves the best and most accurate results.

关键词： Cardiotocography ML FML fetal disease bio-signal

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Mathematical Modeling and Analysis of Patterns in Structured Collections of Big Data 13

Mathematical Modeling and Analysis of Patterns in Structured...

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13th International Conference on Advanced computer Information Technologies, ACIT 2023

作者： Syrotkina, Olena Kobti, Ziad Aleksieiev, Mykhailo Moroz, Borys Udovyk, Iryna Shvachych, Hennadii University of Windsor School of Computer Science Windsor Canada Dnipro University of Technology Department of Software Engineering Dnipro Ukraine

This paper addresses the issue of creating and applying mathematical models and methods for finding generalized solutions when working with structured collections of 'big data'. We reviewed the modern methodologies used to solve problems of this class. The mathematical model presented describes an ordered set of all subsets formed from a finite ordered base set of arbitrary size and data type. We explored a set of functional dependencies of five discrete input variables to work with this mathematical model. Some of these functional dependencies are derived for specific solutions with specified boundary conditions. The paper also presents examples of how the derived functional dependencies are applied in the implementation of mathematical methods using this model. This required us to conduct a comparative assessment of the search time for a solution with and without the use of these mathematical methods. Comparative graphs are demonstrated to show the rate of increase in the number of operations depending on the size of the original finite base set with and without the use of these mathematical methods. As a result of this, logical conclusions are drawn regarding the impact of mathematical methods for working with structured collections on minimizing time and computational resources. © 2023 IEEE.

关键词： Big data

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Deeply‐Recursive Attention Network for video steganography

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CAAI Transactions on Intelligence technology 2023年第4期8卷 1507-1523页

作者： Jiabao Cui Liangli Zheng Yunlong Yu Yining Lin Huajian Ni Xin Xu Zhongfei Zhang College of Computer Science and Technology Zhejiang UniversityHangzhouChina School of Software Technology Zhejiang UniversityNingboChina College of Information Science and Electronic Engineering Zhejiang UniversityHangzhouChina Shanghai SUPREMIND Technology Co. Ltd.ShanghaiChina College of Intelligence Science and Technology National University of Defense TechnologyChangshaChina Department of Computer Science Binghamton UniversityBinghamtonNew YorkUSA

Video steganography plays an important role in secret communication that conceals a secret video in a cover video by perturbing the value of pixels in the cover *** is the first and foremost requirement of any steganographic *** by the fact that human eyes perceive pixel perturbation differently in different video areas,a novel effective and efficient Deeply‐Recursive Attention Network(DRANet)for video steganography to find suitable areas for information hiding via modelling spatio‐temporal attention is *** DRANet mainly contains two important components,a Non‐Local Self‐Attention(NLSA)block and a Non‐Local Co‐Attention(NLCA)***,the NLSA block can select the cover frame areas which are suitable for hiding by computing the correlations among inter‐and intra‐cover *** NLCA block aims to effectively produce the enhanced representations of the secret frames to enhance the robustness of the model and alleviate the influence of different areas in the secret ***,the DRANet reduces the model parameters by performing similar operations on the different frames within an input video *** results show the proposed DRANet achieves better performance with fewer parameters than the state‐of‐the‐art competitors.

关键词： data privacy video processing

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SESAME: Toward Medical Image Segmentation via Foundation Model-assisted Semi-supervised Learning

SESAME: Toward Medical Image Segmentation via Foundation Mod...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Hu, Qiyun Shen, Junao Ji, Jinkang Wang, Xinyu Feng, Tian Cui, Hui Zhejiang University School of Software Technology Hangzhou China La Trobe University Department of Computer Science and Info. Tech. Melbourne Australia

ISBN: (纸本)9798350386226

Medical image segmentation is essential for diagnosis but requires expensive and time-consuming labeled data. Semi-supervised learning (SSL) mitigates this issue by using unlabeled data to improve generalization. However, current SSL methods encounter issues with inadaptive perturbations and low-quality pseudo-labels. Vision foundation models, such as SAM, have shown promise in segmentation. We propose SESAME, an SSL method integrating SAM and U-Net to improve labeling accuracy through a foundation model-assisted pipeline. In particular, we introduce a reliability score to address low-quality pseudo-labels and employ strategies for utilizing both reliable and unreliable images. Reliable images are associated with refined pseudo-labels via a conflict resolving strategy, whereas unreliable ones undergo a mutual region swapping strategy. Extensive experiments demonstrate that our SESAME outperforms representative methods for medical image segmentation. © 2024 IEEE.

关键词： Self-supervised learning

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Discriminative Word Alignment Over Multiple Word Segmentations

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Chinese Journal of Electronics 2025年第2期23卷 263-270页

作者： Ning Xi Xinyu Dai Shujian Huang Jiajun Chen Department of Computer Science and Technology State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

Conventional bilingual word alignment is conducted on sentence pairs with single word segmentation for languages such as Chinese, viz. Single-segmentation-based word alignment (SSWA). However, SSWA may run the risk of losing optimal word segmentation granularities or causing data sparseness in word alignment. This paper proposes Multiple-segmentation-based word alignment (MSWA). In MSWA, diverse and complementary knowledge in multiple word segmentations can be employed to lower the above risks in word alignment. Given $k$ word segmentations of a Chinese sentence, a skeleton segmentation is firstly constructed. The alignment between the skele-ton segmentation and the parallel English sentence is log-linearly modeled, where various features defined over multiple word segmentations are incorporated. The Viterbi alignment, the alignment with the highest score, is mapped back to $k$ word alignments based on $k$ segmentations respectively. Experimentally, MSWA outperformed SSWA on all $k$ segmentations in both alignment quality and translation performance.

关键词： Translation Viterbi algorithm Social sciences Education Medical services Skeleton

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A NOVEL INTELLIGENT FAULT OBSERVER TO DIAGNOSE ACTUATOR FAULT AND SENSOR NOISE BASED ON PROBABILITY DISTRIBUTIONS

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International Journal of Robotics and Automation 2024年第10期39卷

作者： AlBayati, Ahmad Hussain Department of Software Computer Science and Information Technology University of Kirkuk Iraq

This paper aims to bridge the gap between classic model base observer and intelligent observer based on the model base and probability distributions to monitor the plant which is affected by actuator fault and sensor noise simultaneously. The paper includes the implementing and designing of two new optimal actuator observers which also relies on two optimal theories. The first new optimal actuator fault observer is a model base observer based on Lyapunov conditions while, the novelty of the second optimal actuator observer comes from introducing a fault diagnosis algorithm based on a sequential sampling filter (SSF) as a prominent part of the algorithm to filter the diagnosed fault in using the rules of the algorithm. The filters depend on the likelihood ratio, which consists of two hypothetical distributions;the target distribution and the sampling distribution. The new intelligent observer has given rise to innovative algorithms in the field of fault detection and diagnose observer. Moreover, the algorithms verify Lyapunov conditions. In addition, to demonstrate the integrity of the observers, a comparison has been made in the presence of white sensor noise, which is the main reason for inaccurate measurement in addition to two types of actuator faults;Gaussian and non-Gaussian. The study used to verify the performance, where two physical and relative error criteria have been used to evaluate the performance. However, the simulation results appear the flexibility of the second observer to diagnose the actuator fault with sensor noise together due to the number of parameters in diagnosing rules and parameters of the two probabilities. © 2024 Acta Press. All rights reserved.

关键词： fault prognosis (FP) Fault tolerant control (FTC) intelligent optimal actuator fault diagnosis observer (IAFO) optimal actuator fault diagnosis observer (AFO) sequential sampling filter (SSF)

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