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作者： Cui, Xiaoliang Khan, Dawar He, Zhenbang Cheng, Zhanglin Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China Department of Information Technology The University of Haripur Haripur Pakistan

Augmented Virtual Environments (AVE) or Virtual-Reality Fusion systems fuse dynamic videos with static three-dimensional (3D) models of a virtual environment to provide an optimal solution for visualizing and understanding multichannel surveillance systems. However, texture distortion caused by viewpoint changes in such systems is a critical issue that needs to be addressed. To minimize texture fusion distortion, this paper presents a novel virtual environment system in two phases, offline and online phases, to dynamically fuse multiple surveillance videos with a virtual 3D scene. In the offline phase, a static virtual environment is obtained by performing a 3D photogrammetric reconstruction from the input images of the scene. In the online phase, the virtual environment is augmented by fusing multiple videos through two optional strategies. One strategy is to dynamically map images of different videos onto a 3D model of the virtual environment, and the other is to extract moving objects and represent them as billboards. The system can be used to visualize a 3D environment from any viewpoint augmented by real-time videos. Experiments and user studies in different scenarios demonstrate the superiority of our system. © 2022 John Wiley & Sons Ltd.

关键词： 3D modeling

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Enhancing Transformer Tracking Using NF-ResNet and ResNeXT Backbones 1

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1st International Conference on Machine Intelligence for Research and Innovations, MAiTRI 2023

作者： Verma, Sourabh Singla, Rajesh Verma, Om Prakash Sharma, Richa Gupta, Himanshu Sharma, Tarun Muthanna, Ammar Department of Instrumentation and Control Engineering Dr. B R Ambedkar National Institute of Technology Jalandhar Punjab Jalandhar India Department of Computer Science—Internet of Things ABES Institute of Technology Ghaziabad India University Meerut India Saint-Petersburg State University of Telecommunications Saint Petersburg Russia

ISBN: (数字)9789819981359

ISBN: (纸本)9789819981342

Residual networks play a foremost role in the domain of tracking, specifically in the extraction of features. The residual networks are using a simple technique of skipping connections to overcome the problem of vanishing gradient. Also, it is using batch normalization after each layer of convolution to accelerate the training by reducing the dependency over the parameters. Though, we have tried to solve these issues by introducing ResNeXt and NF-ResNet one by one into the TransT tracker as feature extractors in the place of ResNet. Finally, we present a ResNeXt-based TransT tracker (named as TransT_NeXt) and NF-Net-based TransT tracker (named as TransT_NFNet). We have evaluated proposed trackers on three large-scale benchmark datasets and four small-scale benchmark datasets but unfortunately, our trackers haven’t performed well. At last, we have discussed about the future work on the basis of this study. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Large datasets

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Cardiac Clarity: Harnessing Machine Learning for Accurate Heart-Disease Prediction

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IEEE Access 2025年 13卷 97529-97544页

作者： Santhosh, Sujith Chadaga, Krishnaraj Vijaya Arjunan, R. D'Souza, Sandra Manipal Academy of Higher Education Manipal Institute of Technology Department of Instrumentation and Control Engineering Karnataka Manipal576104 India Manipal Academy of Higher Education Manipal Institute of Technology Department of Computer Science and Engineering Karnataka Manipal576104 India

The major contributor to global mortality is cardiovascular disease, posing a formidable challenge to the global healthcare system. Heart disease often develops and progresses without noticeable symptoms, emphasizing the need for earlier detection to prevent severe outcomes. AI models provide tools for accurate diagnosis, data analysis, and predictive diagnosis of people who may be suffering from heart disease. But increasingly, solutions like Explainable AI (XAI) are gaining traction. XAI demystifies the decisions of AI models, making them transparent and understandable to the point where they boost confidence in the context of AI diagnostics by healthcare professionals;the AI then provides support to professionals to decide the best course of action for each patient based on the analysis. XAI techniques such as SHAP, LIME, QLattice and Anchor are popular, and have been used in this study. We analysed a heart disease dataset collected from a multispecialty hospital in India, which is publicly available on Mendeley. We used ML models, including RF, Logistic Regression, KNN, XGBoost, DT and SVM. These models were optimized and stack-ensembled to form the 'CARDIACX' model to predict the risk of heart disease. The models were fine-tuned using Grid Search, Random Search, and Bayesian Optimization methods, achieving promising results with Random Forest achieving an AUC = 0.99 and accuracy of 98.5%, outperforming all the other models and demonstrating robust performance on various metrics such as AUC, PR Curve, Log Loss, Jaccard Score, and MCC. This study provides a reliable and transparent framework for early detection of heart disease, providing useful information and enabling patients to receive timely and personalized care. © 2013 IEEE.

关键词： Logistic regression

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Personalized Federated Learning with Kolmogorov-Arnold Enhanced CNN for Rotating Equipment Fault Diagnosis 21

Personalized Federated Learning with Kolmogorov-Arnold Enhan...

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21st International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Kulevome, Delanyo Kwame Bensah Man, Qiu Mawuli, Ernest Smith Opoku-Mensah, Edward Qilu Institute of Technology Department of Computer and Information Engineering Shandong Jinan250200 China Qilu Institute of Technology Department of Intelligent Manufacturing and Control Engineering Shandong Jinan250200 China Akenten Appiah-Menka University of Skills Training and Entrepreneurial Development Department of Electrical/Electronic Technology Education Kumasi Ghana HEC Department of Information Technologies Montréal Canada

ISBN: (纸本)9798331519254

Fault diagnosis of rotating equipment plays a crucial role in ensuring operational reliability and minimizing downtime in industrial systems. This study proposes a novel approach that integrates personalized federated learning (PFL) with a Kolmogorov-Arnold network (KAN)-enhanced convolutional neural network (CNN) to improve diagnostic accuracy across heterogeneous operational environments. The framework is designed to address variations in equipment conditions and data distributions, providing personalized models tailored to specific clients. A comprehensive evaluation is conducted using benchmark datasets, demonstrating significant improvements in fault classification accuracy, model interpretability, and training efficiency under non-IID data settings using the KAN-enhanced CNN model. This study contributes to advancing predictive maintenance practices, paving the way for resilient and intelligent industrial systems. © 2024 IEEE.

关键词： Federated learning

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FedVC: Virtual Clients for Federated Learning with Imbalanced Label Distribution 24

FedVC: Virtual Clients for Federated Learning with Imbalance...

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3rd International Conference on Algorithms, Data Mining, and Information Technology, ADMIT 2024

作者： Chen, Zhouguo Chen, Hongda Liu, Shuai Liu, Yongdong Schoolof Computer Science and Engineering Southeast University Jiangsu Nanjing China The 30th Research Institute of China Electronics Technology Group Corporation Sichuan Chengdu China School of Software Engineering Xian Jiaotong University Shaanxi Xian China China Industrial Control Systems Cyber Emergency Response Team Beijing China

ISBN: (纸本)9798400718120

Federated Learning (FL) has gained considerable attention for collaborative training in big data analysis, particularly in terms of privacy and communication constraints. Despite its promising advantages, FL faces the challenge of dealing with imbalanced label distribution on edge devices, which complicates the learning task. In literature, numerous studies have attempted to tackle this issue by introducing additional hyperparameters, which can complicate and prolong the process of model training and tuning. This complexity may potentially lead to overfitting and limited performance on new datasets. In this paper, we present a new Federated Learning framework called FedVC, which introduces virtual clients to minimize hyperparameter reliance. FedVC aims to bridge the gap between balanced and imbalanced label distribution. Compared with traditional federated learning frameworks, the proposed FedVC model designs a dynamically balanced dataset by utilizing virtual clients, which is proposed for the first time in this work. These virtual clients determine which part of the data should be used in one single training round and manage backpropagation timing for local real clients. The balanced dataset promotes greater stability in local models, thereby enhancing the overall stability of the training process. The experimental results show that FedVC outperforms classical FedAvg and the compared baseline methods for the data with imbalanced label distribution. Especially, FedVC exhibits more stable and accurate results than alternative approaches in highly heterogeneous settings. The source code is available at https://***/aikedaerC/FedVC. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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Enhancing Object Detection Robustness for Cross-Depiction Through Neural Style Transfer 8

Enhancing Object Detection Robustness for Cross-Depiction Th...

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8th International Conference of Yearly Reports on Informatics, Mathematics, and engineering, ICYRIME 2023

作者： Fiani, Francesca Puglisi, Adriano Napoli, Christian Department of Computer Control and Management Engineering Sapienza University of Rome Roma00185 Italy Institute for Systems Analysis and Computer Science Italian National Research Council Roma00185 Italy Department of Computational Intelligence Czestochowa University of Technology Czestochowa42-201 Poland

Modern neural networks models for computer vision are trained on millions of images. The idea is that models are able to increase generalization when the dataset contains well diversified images, e.g. with varied illumination and environmental conditions of the same objects. Generalization is particularly relevant in object detection, especially for what concerns the cross-depiction problem. In this work we explore the use of Neural Style Transfer as a novel technique to morph the original data, with the aim to enhance model generalization. To verify the effect on performances for object detection models, we selected the Faster R-CNN model to be applied on the Pascal VOC 2012 dataset. A number of tests were performed through style variations on images and by tuning Neural Style Transfer parameters to maintain the content of the original images. The experiments showed promising results, which effectively provide a foundation for future studies on cross-depiction via Neural Style Transfer. © 2023 Copyright for this paper by its authors.

关键词： Object detection

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Verification of Real Time Operating System Exception Management Based on SPARCv8

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Journal of computer Science & Technology 2021年第6期36卷 1367-1387页

作者： Zhi Ma Lei Qiao Meng-Fei Yang Shao-Feng Li Jin-Kun Zhang Beijing Institute of Control Engineering Beijing 100190China China Academy of Space Technology Beijing 100094China School of Computer Science and Technology Xidian UniversityXi'an 710071China

Exception management,as the lowest level function module of the operating system,is responsible for making abrupt changes in the control flow to react to exception events in the *** correctness of the exception management is crucial to guaranteeing the safety of the whole ***,existing formal verification projects have not fully considered the issues of exceptions at the assembly *** for real-time operating systems,in addition to basic exception handling,there are nested exceptions and task switching by exceptions service *** our previous work,we used high-level abstraction to describe the basic elements of the exception management and verified correctness only at the requirement *** on earlier work,this paper proposes EMS(Exception Management SPARCv8),a practical Hoare-style program framework to verify the exception management based on SPARCv8(Scalable Processor Architecture Version 8)at the design *** framework describes the low-level details of the machine,such as registers and memory *** divides the execution logic of the exception management into six phases for comprehensive formal *** the executing scenario of the real-time operating system SpaceOS on the Beidou-3 satellite as an example,we use the EMS framework to verify the exception *** the formalization and proofs are implemented in the interactive theorem prover Coq.

关键词： operating system exception Scalable Processor Architecture Version 8(SPARCv8) Coq formal verification

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Application of Deep Learning Models Based on Chaos Modeling in Power Internet of Things Forecasting Tasks 10th

Application of Deep Learning Models Based on Chaos Modeling ...

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10th International Conference of Pioneering computer Scientists, Engineers and Educators, ICPCSEE 2024

作者： Miao, Jiahao Li, Zhixin Long, Yang School of Information and Control Engineering Jilin Institute of Chemical Technology Jilin132022 China School of Computer Technology and Engineering Changchun Institute of Technology Changchun130012 China

ISBN: (纸本)9789819787425

Deep learning has provided a reliable technological foundation for time series prediction tasks. In the field of time series prediction, deep learning models are widely used in the Power Internet of Things due to their powerful fitting capabilities. However, efficiently capturing the relationships between time data remains a challenge. To overcome this challenge, this paper proposes a time series prediction model called the CTN-former based on chaos theory and a self-attention mechanism. CTN-former can detect chaos in time series through a carefully designed chaotic theory network (CTN), derive the relationships between time data, and perform phase space reconstruction. Additionally, to improve efficiency, the model includes a master-slave self-attention module to reduce quadratic complexity. Finally, experiments were conducted on four large-scale public datasets from three different domains. The results demonstrate that compared to traditional time series prediction models, the CTN-former achieves a relative improvement rate of 12.7% in time series prediction tasks, and the CTN-former achieves a relative improvement rate of 12.7% in time series prediction tasks and provides a novel solution for time series prediction by recovering the motion trajectory between chaotic time series, thereby enhancing the data utilization efficiency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Prediction models

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Invariant Set Analysis and control Synthesis of Multi-Equilibrium Switched Systems under control Constraints

Invariant Set Analysis and Control Synthesis of Multi-Equili...

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International Conference on Intelligent control and Information Processing (ICICIP)

作者： Yuejiang Han Lixian Zhang Yihang Ding Yifei Dong Ye Liang School of Astronautics Harbin Institute of Technology Harbin China College of Computer and Control Engineering Northeast Forestry University Harbin China

ISBN: (数字)9798331516147

ISBN: (纸本)9798331516154

This paper investigates the problems of invariant set analysis and control synthesis for multi-equilibrium switched systems under control constraints. A control strategy based on the invariant set method is proposed, accompanied by criteria formulated as linear matrix inequalities, ensuring that the state remains within the mode-dependent invariant sets. Furthermore, considering the control constraints in practical scenarios, a multi-equilibrium switched controller with control constraints (MESCC controller) is designed. Compared to existing works, the proposed method incorporates mode-dependent dwell time conditions and mode-dependent invariant sets, thereby offering enhanced design flexibility. An example of an aero-engine control system is presented to demonstrate the effectiveness and potential of the theoretical results.

关键词： Switched systems Robust control Switches Information processing control systems Linear matrix inequalities Closed loop systems Aircraft propulsion Intelligent control

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Student modeling considering learning behavior history with deep factorization machines 27

Student modeling considering learning behavior history with ...

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27th International Conference on Knowledge Based and Intelligent Information and engineering Sytems, KES 2023

作者： Kakizaki, Toma Oeda, Shinichi Advanced Course of Control and Information Engineering National Institute of Technology Kisarazu College 11-1 Kiyomidaihigashi 2-chome Chiba Kisarazu City Japan Department of Information and Computer Engineering National Institute of Technology Kisarazu College 11-1 Kiyomidaihigashi 2-chome Chiba Kisarazu City Japan

To use an intelligent tutoring system (ITS) in an educational setting effectively, it is necessary to understand the skill status of students and recommend appropriate questions. Existing studies focused on improving the performance of ITS using student modeling to estimate the skill status of students. Knowledge tracing (KT) is the mainstream student modeling method, and deep learning approaches such as deep knowledge tracing and self-attentive knowledge tracing have been studied extensively in recent years. These models take only the questions solved by the student and the correct or incorrect answers to those questions as input;they do not assume the use of other features. In this study, we perform student modeling using DeepFM and FiBiNET that combines factorization machines and deep learning. Our results indicate that these models are more accurate than KT because of their ability to cope with sparse data and consider pairwise feature interactions, and more suited for real-world applications than KT. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： DeepLearning Educational data mining Factorization machines Intelligent tutoring system Knowledge tracing Student modeling

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