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IEEE Conference on Global Communications (GLOBECOM)

作者： Michele Pacenti Mark F. Flanagan Dimitris Chytas Bane Vasić Department of Electrical and Computer Engineering University of Arizona Tucson AZ USA School of Electrical and Electronic Engineering University College Dublin Ireland

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

We propose a novel bit-flipping (BF) decoder tailored for toric codes. We introduce the proximity vector as a heuristic metric for flipping bits, and we develop a new subroutine for correcting a particular class of harmful degenerate errors. Comparing to other decoders, our algorithm is particularly suitable for efficient hardware implementation as it does not require operations on dynamic memories. The proposed decoder shows a decoding threshold of 7.5% for the 2D toric code over the binary symmetric channel.

关键词： Measurement Codes Heuristic algorithms Memory management Hardware Vectors Decoding Error correction codes Global communication

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Microresonator Frequency Combs Involving Both $\chi^{(2)}$ and $\chi^{(3)}$ Nonlinearities

Microresonator Frequency Combs Involving Both $\chi^{(2)}$ a...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Xiaoxiao Xue Kaiyi Wu Andrew M Weiner Department of Electronic Engineering Tsinghua University Beijing China School of Electrical and Computer Engineering Purdue University West Lafayette Indiana USA

Many microresonator platforms for frequency comb generation have both $\chi^{2}$ and $\chi^{(3)}$ nonlinearities. Here we discuss several comb formation behaviors in this scenario, including second-harmonic assist... 详细信息

ISBN: (纸本)9798350369311

关键词： Four-wave mixing Solitons Lasers and electrooptics Frequency conversion Microcavities Regulation Electro-optical waveguides

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A Novel Three-Stage PolSAR Ship Detection Framework based on Deep Learning

A Novel Three-Stage PolSAR Ship Detection Framework based on...

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Signal, Information and Data Processing (ICSIDP), IEEE International Conference on

作者： Junfan Xue Junjun Yin Jian Yang School of Computer & Communication Engineering University of Science and Technology Beijing Beijing China Department of Electronic Engineering Tsinghua University Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Polarimetric Synthetic Aperture Radar (PolSAR) image ship detection is one of the crucial applications of PolSAR systems and holds significant research value. However, current deep learning-based polarimetric SAR image ship detection methods face challenges such as difficult post-processing and slow detection speed. To solve the above problems, we propose a novel Three-Stage Polarimetric SAR Ship Detection Framework based on deep learning. The main concept is to use a sliding window to extract image slice samples, then employ the Ship Coarse Location Module (SCLM) to identify slice samples containing ships. Next, the Polarization Feature Enhancement Module (PFEM) enhances the features of these slice samples to reduce the feature distance of pixels of the same category and increase the feature distance of pixels of different categories. Afterward, the enhanced slice samples are fed into the KAN Vision Transformer Detection Module (KVITDM) for patch-level detection. Finally, decision fusion is applied to combine the detection results generated from different initial sliding window positions. Experimental results show that the proposed method has good performance in different scenes and outperforms traditional and existing deep learning-based polarimetric SAR ship detection methods.

关键词： Deep learning computer vision Accuracy Object detection Interference Transformers Feature extraction Polarimetric synthetic aperture radar Radar polarimetry Marine vehicles

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A Low Complexity ML-Based Methods for Malware Classification

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computers, Materials & Continua 2024年第9期80卷 4833-4857页

作者： Mahmoud E.Farfoura Ahmad Alkhatib Deema Mohammed Alsekait Mohammad Alshinwan Sahar A.El-Rahman Didi Rosiyadi Diaa Salama Abd Elminaam Cybersecurity Department Al-Zaytoonah University of JordanAmman11733Jordan Department of Computer Science and Information Technology Applied CollegePrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Faculty of Information Technology Applied Science Private UniversityAmman11931Jordan Computer Systems Program-Electrical Engineering Department Faculty of Engineering-ShoubraBenha UniversityCairo11629Egypt Research Center for Artificial Intelligence and Cybersecurity Electronics and Informatics OrganizationNational Research and Innovation Agency(BRIN)KST Samaun SamadikunBandung40135Republic of Indonesia Jadara Research Center Jadara UniversityIrbid21110Jordan MEU Research Unit Middle East UniversityAmman11831Jordan

The article describes a new method for malware classification,based on a Machine Learning(ML)model architecture specifically designed for malware detection,enabling real-time and accurate malware *** an innovative feature dimensionality reduction technique called the Interpolation-based Feature Dimensionality Reduction Technique(IFDRT),the authors have significantly reduced the feature space while retaining critical information necessary for malware *** technique optimizes the model’s performance and reduces computational *** proposed method is demonstrated by applying it to the BODMAS malware dataset,which contains 57,293 malware samples and 77,142 benign samples,each with a 2381-feature *** the IFDRT method,the dataset is transformed,reducing the number of features while maintaining essential data for accurate *** evaluation results show outstanding performance,with an F1 score of 0.984 and a high accuracy of 98.5%using only two reduced *** demonstrates the method’s ability to classify malware samples accurately while minimizing processing *** method allows for improving computational efficiency by reducing the feature space,which decreases the memory and time requirements for training and *** new method’s effectiveness is confirmed by the calculations,which indicate significant improvements in malware classification accuracy and *** research results enhance existing malware detection techniques and can be applied in various cybersecurity applications,including real-timemalware detection on resource-constrained *** and scientific contribution lie in the development of the IFDRT method,which provides a robust and efficient solution for feature reduction in ML-based malware classification,paving the way for more effective and scalable cybersecurity measures.

关键词： Malware detection ML-based models dimensionality reduction feature engineering

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TEMPORAL GENERALIZATION ESTIMATION IN EVOLVING GRAPHS 12

TEMPORAL GENERALIZATION ESTIMATION IN EVOLVING GRAPHS

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12th International Conference on Learning Representations, ICLR 2024

作者： Lu, Bin Ma, Tingyan Gan, Xiaoying Wang, Xinbing Zhu, Yunqiang Zhou, Chenghu Liang, Shiyu Department of Electronic Engineering Shanghai Jiao Tong University China Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences China John Hopcroft Center for Computer Science Shanghai Jiao Tong University China

Graph Neural Networks (GNNs) are widely deployed in vast fields, but they often struggle to maintain accurate representations as graphs evolve. We theoretically establish a lower bound, proving that under mild conditions, representation distortion inevitably occurs over time. To estimate the temporal distortion without human annotation after deployment, one naive approach is to pre-train a recurrent model (e.g., RNN) before deployment and use this model afterwards, but the estimation is far from satisfactory. In this paper, we analyze the representation distortion from an information theory perspective, and attribute it primarily to inaccurate feature extraction during evolution. Consequently, we introduce SMART, a straightforward and effective baseline enhanced by an adaptive feature extractor through self-supervised graph reconstruction. In synthetic random graphs, we further refine the former lower bound to show the inevitable distortion over time and empirically observe that SMART achieves good estimation performance. Moreover, we observe that SMART consistently shows outstanding generalization estimation on four real-world evolving graphs. The ablation studies underscore the necessity of graph reconstruction. For example, on OGB-arXiv dataset, the estimation metric MAPE deteriorates from 2.19% to 8.00% without reconstruction. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Information theory

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Hijaiyah Letter Learning Education Game for Children with Cerebral Palsy Elementary School Level Using Dance Pad Media

Hijaiyah Letter Learning Education Game for Children with Ce...

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International Conference on informatics and Computing (ICIC)

作者： Endah Sudarmilah Ainayah Syifa Hendri Devi Afriyantari Puspa Putri Wiwien Dinar Pratisti Fatah Yasin Al Irsyadi Ika Safitri Windiarti Informatics Engineering Department Faculty of Communication and Informatics University Muhammadiyah Surakarta Surakarta Indonesia Psychology Department Faculty of Psychology University Muhammadiyah Surakarta Surakarta Indonesia Computer Science Department Engineering Faculty Universitas Muhammadiyah Palangkaraya Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Children with special needs cerebral palsy are children who have movement disorders, muscle tone, or posture caused by damage to the immature and developing brain. The learning method for children with cerebral palsy is different from normal children in general. The learning method used must adhere to the same principles, namely interesting, easy to accept and remember, and must be adapted to the ability to control motion. One method that can be used in the learning process of children with cerebral palsy is using game media. This study aims to design and create an educational game for learning hijaiyah letters that are tailored to the needs and motor training of Cerebral Palsy children. The research was conducted at the MI Muhammadiyah PK Kartasura School with the needs of the school as the main reference. This game is developed using 2D Game engine as the main application in making games and a dance pad as a liaison between the game and the controller. The design of this game uses the SDLC (System Development Life Cycle) method with the Waterfall model and is tested using the black box testing method and the SUS (System Usability Scale) method. The expected result of this research is an educational game that is easy to play by children with Cerebral Palsy in the light category, very interesting, can be used to train children’s motor skills, and can be used by teachers as an interactive and fun learning medium. Based on the black box testing, it shows that the game is running as expected, moreover the SUS testing got 70,625 score which was included in the Good category.

关键词： Learning systems Training Cerebral palsy Humanities Games Media Muscles Motors Usability Testing

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Vehicle Tracking System in Drone Imagery with YOLOv5 and Histogram

Vehicle Tracking System in Drone Imagery with YOLOv5 and His...

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SICE Annual Conference

作者： Jehwan Choi Seongbo Ha Youlkyeong Lee Kanghyun Jo Department of Electrical Electronic and Computer Engineering University of Ulsan Ulsan Korea

In this study, we propose a vehicle tracking system targeting drone footage. The proposed system utilizes the real-time object detection network, YOLOv5, to acquire vehicle location information and segment the vehicle regions based on it. The system analyzes the histogram of the segmented regions, compares them with past frames, and determines whether the objects are identical to perform tracking. To enhance the efficiency of histogram comparison, the algorithm is designed to compare objects only within a certain radius using coordinate information and past frame object data. The MOTA (Multi Object Tracking Accuracy), a representative tracking evaluation metric, showed 90%. However, it is important to consider the limited environment of data usage and experiments. The results of this study suggest that the real-time performance of the vehicle tracking system can be utilized in various fields such as traffic control, vehicle management, and accident response.

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Hajj Crowd Management Using CNN-Based Approach

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computers, Materials & Continua 2021年第2期66卷 2183-2197页

作者： Waleed Albattah Muhammad Haris Kaka Khel Shabana Habib Muhammad Islam Sheroz Khan Kushsairy Abdul Kadir Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Electronic Section Universiti Kuala Lumpur British Malaysian InstituteMalaysia Department of Electrical Engineering Onaizah CollegesSaudi Arabia Department of Electrical and Computer Engineering International Islamic UniversityMalaysia

Hajj as the Muslim holy pilgrimage,attracts millions of humans to Mecca every *** to statists,the pilgrimage has attracted close to 2.5 million pilgrims in 2019,and at its peak,it has attracted over 3 million pilgrims in *** is considered as the world’s largest human *** makes one of the main concerns with regards to managing the large crowds and ensuring that stampedes and other similar overcrowding accidents are *** paper presents a crowd management system using image classification and an alarm system for managing the millions of crowds during *** image classification system greatly relies on the appropriate dataset used to train the Convolutional neural network(CNN),which is the deep learning technique that has recently attracted the interest of the research community and industry in varying applications of image classification and speech *** core building block of CNN is is a convolutional layer obtained by the getting CNN trained with patches bearing designated features of the trainee *** algorithm is implemented,using the Conv2D layers to activate the CNN as a sequential ***,creating a 2D convolution layer having 64 filters and drop out of 0.5 makes the core of a CNN referred to as a set of *** aim is to train the CNN model with mapped image data,and to make it available for use in classifying the crowd as heavily-crowded,crowded,semi-crowded,light crowded,and *** utility of these results lies in producing appropriate signals for proving helpful in monitoring the *** pilgrims from the photos will help the authorities to determine the number of people in certain *** results demonstrate the utility of agent-based modeling for Hajj pilgrims.

关键词： Crowd management CNN approach Hajj

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Generative AI in Academic Writing: A Comparison of DeepSeek, Qwen, ChatGPT, Gemini, Llama, Mistral, and Gemma

arXiv

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arXiv 2025年

作者： Aydın, Ömer Karaarslan, Enis Erenay, Fatih Safa Džakula, Nebojša Bačanin Manisa Celal Bayar University Faculty of Engineering and Natural Sciences Department of Computer Sciences Manisa Turkey University of Waterloo Faculty of Engineering Management Science and Engineering Ontario Canada Mugla Sitki Kocman University Faculty of Engineering Department of Computer Engineering Mugla Turkey Singidunum University Faculty of Informatics and Computing Belgrade Serbia

Deepseek and Qwen LLMs became popular at the beginning of 2025 with their low-cost and open-access LLM solutions. A company based in Hangzhou, Zhejiang, China, announced its new LLM, DeepSeek v3, in December 2024. Then, Alibaba released its AI model, Qwen 2.5 Max, on January 29, 2025. These tools, which are free and open-source have made a significant impact on the world. Deepseek and Qwen also have the potential to be used by many researchers and individuals around the world in academic writing and content creation. Therefore, it is important to determine the capacity of these new LLMs to generate high-quality academic content. This study aims to evaluate the academic writing performance of both Qwen 2.5 Max and DeepSeek v3 by comparing these models with popular systems such as ChatGPT, Gemini, Llama, Mistral, and Gemma. In this research, 40 articles on the topics of Digital Twin and Healthcare were used. The method of this study involves using generative AI tools to generate texts based on posed questions and paraphrased abstracts of these 40 articles. Then, the generated texts were evaluated through the plagiarism tool, AI detection tools, word count comparisons, semantic similarity tools and readability assessments. It was observed that plagiarism test result rates were generally higher for the paraphrased abstract texts and lower for the answers generated to the questions, but both were above acceptable levels. In the evaluations made with the AI detection tool, it was determined with high accuracy that all the generated texts were detected as AI-generated. In terms of the generated word count comparison, it was evaluated that all chatbots generated satisfactory amount of content. Semantic similarity tests show that the generated texts have high semantic overlap with the original texts. The readability tests indicated that the generated texts were not sufficiently readable. © 2025, CC BY.

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COMPARATIVE PERFORMANCE OF MACHINE LEARNING MODELS FOR TEMPERATURE PREDICTION IN VANADIUM REDOX FLOW BATTERIES

COMPARATIVE PERFORMANCE OF MACHINE LEARNING MODELS FOR TEMPE...

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International Conference on Green Energy, Computing and Intelligent Technology 2024, GEn-CITy 2024

作者： Zhang, Anxiu Juwono, Filbert H. Li, Hongfei Purwanto, Erick Wong, W.K. Liu, Linfeng Telecommunications Engineering Program Xi'an Jiaotong-Liverpool University Suzhou China Department of Electrical and Electronic Engineering Xi'an Jiaotong-Liverpool University Suzhou China Applied Mathematics Program Xi'an Jiaotong-Liverpool University Suzhou China Department of Computing Xi'an Jiaotong-Liverpool University Suzhou China Department of Electrical and Computer Engineering Curtin University Malaysia Miri Malaysia Sustainable Energy Technology Program Xi'an Jiaotong-Liverpool University Suzhou China

ISBN: (纸本)9781837242689

Vanadium Redox Flow Batteries (VRFB) are promising for large-scale energy storage due to their long life and environmental benefits. Accurate temperature prediction is key to optimizing VRFB performance and longevity. This study compares the performance of four machine learning models, i.e., 1D CNN, Particle Swarm Optimization - Support Vector Regressor (PSO-SVR), Decision Tree (DT), and K-Nearest Neighbors (KNN), using a publicly available dataset. Results show that KNN achieves the best results with test Root Mean Square Error (RMSE) of 0.0424 (average) and test R2 of 0.9746 (average), demonstrating strong predictive accuracy. 1D CNN, however, shows poor generalization. These findings suggest that non-parametric models like KNN and DT are highly effective for VRFB temperature prediction. © The Institution of engineering & Technology 2024.

关键词： machine learning prediction Vanadium redox flow battery

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