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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Martins, João Conceição, Filipe Gomes, Marco Silva, Vitor Dinis, Rui Instituto de Telecomunicações Portugal University of Coimbra Department of Electrical and Computer Engineering Coimbra3030-290 Portugal Universidade Nova de Lisboa Faculty of Science and Technology Caparica2829-516 Portugal

ISBN: (纸本)9798350368369

The ability to provide reliable data rates across the several intended coverage areas has made massive multiple-input multiple-output (m-MIMO) cell-free (CF) a crucial technology for future sixth-generation (6G) systems. However, CF networks also introduce new vulnerabilities, specifically in terms of security and network integrity. For that, to complement the conventional cryptographic algorithms, physical layer security (PLS) can be an effective strategy for acquiring essential wireless network information in order to create important authentication mechanisms against spoofing attacks. To this end, we propose leveraging the wireless channel and the access point selection (APS) allocation schemes as authentication methods to prevent network attacks that attempt to impersonate legitimate user communications. Our results show that monitoring these parameters provides valuable insights about the network performance and the impact under active eavesdropping influence. Consequently, this approach enables a statistical detection of illegitimate spoofing attempts through alarms, which can be further reinforced by upper-layer authentication protocols to enhance the security performance of wireless networks. © 2025 IEEE.

关键词： Authentication Protocol

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Crowd-Counting of Images in Rainy Weather Based on GAN-U-Net Model and Multi-column CNN Counting Approaches 3

Crowd-Counting of Images in Rainy Weather Based on GAN-U-Net...

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3rd IEEE International Conference on Electronic engineering, ICEEM 2023

作者： Saad, Rasha A. Abd El-Samie, Fathi E. Abdel-Azim, Mohammed El-Hoseny, Heba M. Obour Higher Institute for Engineering and Technology Department of Electronics and Electrical Communications Egypt Menoufia University Faculty of Electronic Engineering Department of Electronics and Electrical Communications Egypt Mansoura University Faculty of Engineering Department of Electronics and Electrical Communications Egypt The Higher Future Institute for Specialized Technological Studies Department of Computer Science Egypt

ISBN: (纸本)9798350323511

A count of crowds tries to determine the total number of persons in crowded places in order to avoid disruption of the safety system and maintain crowd safety. Accurate estimation of the size of crowds is a challenging process because of numerous challenges like occlusion, complicated backgrounds, varying sizes, uneven distribution, alteration of lighting, and adaptable weather. In this study, we propose a solution to two crowd counting challenges: poor weather such as rain and image scaling. We investigate crowd counting in the rainy Shanghai-Tec images. We adopt the GAN-U-Net de-raining model and the SAS-Net [1] and SGA-Net [2] crowd-counting models in our study. © 2023 IEEE.

关键词： computer vision

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GLCF: A Global-Local Multimodal Coherence Analysis Framework for Talking Face Generation Detection 39

GLCF: A Global-Local Multimodal Coherence Analysis Framework...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Chen, Xiaocan Yin, Qilin Liu, Jiarui Lu, Wei Luo, Xiangyang Zhou, Jiantao School of Computer Science and Engineering MoE Key Laboratory of Information Technology Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou510006 China Alibaba Group Hangzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450002 China State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science Faculty of Science and Technology University of Macau 999078 China

ISBN: (纸本)157735897X

Talking face generation (TFG) allows for producing lifelike talking videos of any character using only facial images and accompanying text. Abuse of this technology could pose significant risks to society, creating the urgent need for research into corresponding detection methods. However, research in this field has been hindered by the lack of public datasets. In this paper, we construct the first large-scale multi-scenario talking face dataset (MSTF), which contains 22 audio and video forgery techniques, filling the gap of datasets in this field. The dataset covers 11 generation scenarios and more than 20 semantic scenarios, closer to the practical application scenario of TFG. Besides, we also propose a TFG detection framework, which leverages the analysis of both global and local coherence in the multimodal content of TFG videos. Therefore, a region-focused smoothness detection module (RSFDM) and a discrepancy capture-time frame aggregation module (DCTAM) are introduced to evaluate the global temporal coherence of TFG videos, aggregating multi-grained spatial information. Additionally, a visual-audio fusion module (V-AFM) is designed to evaluate audiovisual coherence within a localized temporal perspective. Comprehensive experiments demonstrate the reasonableness and challenges of our datasets, while also indicating the superiority of our proposed method compared to the state-of-the-art deepfake detection approaches. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Public risks

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Brain Cancer Tumor Classification from Motion-Corrected MRI Images Using Convolutional Neural Network

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computers, Materials & Continua 2021年第8期68卷 1551-1563页

作者： Hanan Abdullah Mengash Hanan A.Hosni Mahmoud Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer and Systems Engineering Faculty of EngineeringUniversity of AlexandriaAlexandriaEgypt

Detection of brain tumors in MRI images is the first step in brain cancer *** accuracy of the diagnosis depends highly on the expertise of ***,automated diagnosis of brain cancer from MRI is receiving a large amount of ***,MRI tumor detection is usually followed by a biopsy(an invasive procedure),which is a medical procedure for brain tumor *** is of high importance to devise automated methods to aid radiologists in brain cancer tumor diagnosis without resorting to invasive *** neural network(CNN)is deemed to be one of the best machine learning algorithms to achieve high-accuracy results in tumor identification and *** this paper,a CNN-based technique for brain tumor classification has been *** proposed CNN can distinguish between normal(no-cancer),astrocytoma tumors,gliomatosis cerebri tumors,and glioblastoma *** implemented CNN was tested on MRI images that underwent a motion-correction *** CNN was evaluated using two performance measurement *** first one is a k-fold cross-validation testing method,in which we tested the dataset using k=8,10,12,and *** best accuracy for this procedure was 96.26%when k=*** overcome the over-fitting problem that could be occurred in the k-fold testing method,we used a hold-out testing method as a second evaluation *** results of this procedure succeeded in attaining 97.8%accuracy,with a specificity of 99.2%and a sensitivity of 97.32%.With this high accuracy,the developed CNN architecture could be considered an effective automated diagnosis method for the classification of brain tumors from MRI images.

关键词： Classification convolutional neural network tumor classification MRI deep learning k-fold cross classification

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Comparison of Q&A Classification Using BiLSTM Based on Closed Domain Knowledge

Comparison of Q&A Classification Using BiLSTM Based on Close...

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Electrical Power, Electronics, Communications, controls and Informatics Seminar (EECCIS)

作者： Alifi Lazuardi Gunawan Fitra Abdurrachman Bachtiar Budi Darma Setiawan Informatics Engineering Department Faculty of Computer Science Brawijaya University Malang Indonesia

ISBN: (数字)9798350379914

ISBN: (纸本)9798350379921

The faculty of computer science, Universitas Brawijaya (Filkom UB) is committed to providing quality services for the users especially internal and external stakeholders, one of which is through the HaloFilkom service. HaloFilkom services have limitations in terms of time. HaloFilkom services are not available 24 hours due to limited working hours. Questions asked by users are not answered directly. This weakness in the HaloFilkom system can be overcome by using a chatbot system. Chatbot is an interactive system that works with natural human language and can work 24 hours. Thus, the current study explores the basic chatbot model by classifying the Q&A in the closed domain knowledge. The dataset in this research is in the form of pairs of questions and answers regarding various topics at the Filkom UB. The knowledge is preprocessed using text preprocessing which includes case folding, tokenization, padding, and tensorization. One of the chatbot models is a generative model. Creating a generative chatbot model can be done using the Seq2Seq model mechanism which consists of an encoder and decoder. The model created consists of four different architectures, namely a model with an LSTM encoder without attention and with attention and a BiLSTM model encoder without attention and with attention. Hyperparameter tuning was conducted to obtain the best hyperparameter combination. The experiment results show the best hyperparameter combination obtained is hidden size 448, drop out rate 0.5, learning rate 0.001, batch size 64, and teacher force 0. The model with the best loss is obtained with a BiLSTM encoder architecture without an attention mechanism with a train loss of 0.120. The model with the highest BLEU Score was obtained by a model with a BiLSTM encoder architecture without an attention mechanism with a BLEU Score of 0.8587 on the training data. Testing using prompt testing obtained an average BLEU Score of 0.3745 on the BiLSTM encoder without an attention mechanism mo

关键词： Attention mechanisms Force Bidirectional long short term memory Training data computer architecture Chatbots Data models Space exploration Tuning Testing

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Reversible Watermarking Method with Low Distortion for the Secure Transmission of Medical Images

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computer Modeling in engineering & sciences 2022年第3期130卷 1309-1324页

作者： Rizwan Taj Feng Tao Shahzada Khurram Ateeq Ur Rehman Syed Kamran Haider Akber Abid Gardezi Saima Kanwal School of Computer and Communication Lanzhou University of TechnologyLanzhouChina Faculty of Computing The Islamia University of BahawalpurBahawalpurPakistan Department of Electrical Engineering Government College UniversityLahorePakistan College of Internet of Things Engineering Hohai UniversityChangzhouChina Department of Computer Science COMSATS University IslamabadIslamabadPakistan

In telemedicine,the realization of reversible watermarking through information security is an emerging research ***,adding watermarks hinders the distribution of pixels in the cover image because it creates distortions(which lead to an increase in the detection probability).In this article,we introduce a reversible watermarking method that can transmit medical images with minimal distortion and high *** proposed method selects two adjacent gray pixels whose least significant bit(LSB)is different from the relevant message bit and then calculates the distortion *** use the LSB pairing method to embed the secret matrix of patient record into the cover image and exchange pixel *** results show that the designed method is robust to different attacks and has a high PSNR(peak signal-to-noise ratio)*** MRI image quality and imperceptibility are verified by embedding a secret matrix of up to 262,688 bits to achieve an average PSNR of 51.657 *** addition,the proposed algorithm is tested against the latest technology on standard images,and it is found that the average PSNR of our proposed reversible watermarking technology is higher(i.e.,51.71 dB).Numerical results show that the algorithm can be extended to normal images and medical images.

关键词： LSB reversible watermarking medical images security data distortion

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Facial Emotion Recognition Using a Local Binary Pattern based Deep Learning 2

Facial Emotion Recognition Using a Local Binary Pattern base...

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2nd International Conference on computer, Communication and control, IC4 2024

作者： Tayubi, Iftikhar Aslam Pawar, Dilip N. Kiran, Ajmeera Reddy, Pundru Chandra Shaker Sharma, Nipun Chitra, D. King Abdulaziz University Faculty of Computing and Information Technology Rabigh Jeddah Saudi Arabia Bharati Vidyapeeth College of Engineering Instrumentation Engineering Maharashrra Navi Mumbai India Mlr Institute of Technology Dundigal Department of Computer Science and Engineering Telangana Hyderabad India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal506371 India Presidency University Electronics and Communication Engineering Karnataka Bangalore India Panimalar Engineering College Master of Business Administration Tamil Nadu Chennai India

ISBN: (纸本)9798350387933

Facial-emotion-recognition(FER) is being conducted with the goals of analyzing the psychological characteristics of juvenile offenders and promoting the use of deep learning to the extraction of psychological features. Initially, we talk about how certain personality traits are linked to being good at reading facial expressions. To build a facial emotion identification model from this data, we add more convolutional layers to a CNN and combine it with other neural networks like VGGNet, AlexNet, and LeNet-5. Next, a Central-Local-Binary-Pattern(CLBP) method, which is optimized, is incorporated into the CNN to build a CNN-CLBP framework for FER. After face photos have been preprocessed and key parameters have been optimized, the algorithm is subjected to a validity study. The average detection rate for the CNN-CLBP is 88.16%, which is greater than the recognition rate for other approaches. In addition, image preparation and parameter tuning boost the recognition accuracy of this approach without resorting to poor-tting. In addition, the CNN-CLBP system accurately identifies 97% of pleased and startled expressions, but 22.54% of sad ones. The study's findings can be used as a starting point for further investigations into the mental health of teen offenders. © 2024 IEEE.

关键词： Local binary pattern

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Universality Frontier for Asynchronous Cellular Automata

arXiv

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arXiv 2025年

作者： Baburin, Ivan Cook, Matthew Grötschla, Florian Plesner, Andreas Wattenhofer, Roger Department of Computer Science ETH Zürich Switzerland Faculty of Science and Engineering University of Groningen Netherlands Department of Electrical Engineering ETH Zürich Switzerland

In this work, we investigate the computational aspects of asynchronous cellular automata (ACAs), a modification of cellular automata in which cells update independently, following an asynchronous schedule. We introduce flip automata networks (FANs), a simple modification of automata networks that remain robust under any asynchronous update schedule. By using FANs as a middleman, we show that asynchronous automata can efficiently simulate their synchronous counterparts with a linear memory overhead, which improves upon the previously established quadratic bound. Additionally, we address the universality gap for (a)synchronous cellular automata—the boundary separating universal and non-universal automata, which is still not fully understood. We tighten this boundary by proving that all one-way asynchronous automata lack universal computational power. Conversely, we establish the existence of a universal 6-state first-neighbor automaton in one dimension and a 3-state von Neumann automaton in two dimensions, which represent the smallest known universal constructions to date. © 2025, CC BY.

关键词： Sequential machines

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Artificial Intelligence for Monitoring and Evaluation of Cardiopulmonary Resuscitation

SSRN

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SSRN 2024年

作者： Momeny, Mohammad Marhamati, Mahmoud Esfarayen Faculty of Medical Science Esfarayen Iran Department of Computer Engineering Faculty of Engineering Yazd University Yazd Iran

CPR, cardiopulmonary resuscitation, is a life-saving technique that is given typically to someone who is in cardiac arrest. During the education process of CPR, the rescuer's performance is educated and evaluated by human tutors. Our aim is to use artificial intelligence for education and evaluation of rescuers during CPR. Among the criteria for proper CPR administration, the correct and incorrect position of a rescuer is more common and is tracked by human tutors/supervisors. The present study focuses on the detection of the correct and incorrect positions of rescuers for chest compression using deep transfer learning. To deal with the challenge of detecting the correctness of the rescuer’s position, we built a dataset of 76 sequences of the 30-second recorded video of CPR administration with both correct and incorrect positions. We applied the learning to augment strategy to generate new images for data augmentation in order to improve generalizability of deep networks. The proposed method uses a Bayesian optimizer to determine the optimal value of Gaussian and speckle noise parameters for generating new noisy variants of the original image. We trained six state-of-the-art deep networks, such as DarkNet19, EfficientNetB0, GoogleNet, MobileNet-v2, ShuffleNet, and NASNet-Mobile, with the best data augmentation policies to overcome the overfitting problem. Validation of the proposed method in detecting rescuers’ correct position demonstrated the best sensitivity (98.3%), specificity (95.4%), precision (95.5%), recall (98.3%), and F-measure (96.9%) for the pre-trained NASNet-Mobile network when incorporated with both learning-to-augment and conventional data augmentation strategies. © 2024, The Authors. All rights reserved.

关键词： Resuscitation

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Hybrid Single Image Super-Resolution Algorithm for Medical Images

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computers, Materials & Continua 2022年第9期72卷 4879-4896页

作者： Walid El-Shafai Ehab Mahmoud Mohamed Medien Zeghid Anas MAli Moustafa H.Aly Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Electrical Engineering Department College of EngineeringPrince Sattam Bin Abdulaziz UniversityWadi Addwasir11991Saudi Arabia Electrical Engineering Department Aswan UniversityAswan81542Egypt Electronics and Micro-Electronics Laboratory(E.μ.E.L) Faculty of SciencesUniversity of MonastirMonastir5000Tunisia Alexandria Higher Institute of Engineering&Technology(AIET) AlexandriaEgypt Electronics and Communications Engineering Department College of Engineering and TechnologyArab Academy for ScienceTechnology and Maritime TransportAlexandria1029Egypt

High-quality medical microscopic images used for diseases detection are expensive and difficult to ***,low-resolution images are favorable due to their low storage space and ease of sharing,where the images can be enlarged when needed using Super-Resolution(SR)***,it is important to maintain the shape and size of the medical images while enlarging *** of the problems facing SR is that the performance of medical image diagnosis is very poor due to the deterioration of the reconstructed image ***,this paper suggests a multi-SR and classification framework based on Generative Adversarial Network(GAN)to generate high-resolution images with higher quality and finer details to reduce *** proposed framework comprises five GAN models:Enhanced SR Generative Adversarial Networks(ESRGAN),Enhanced deep SR GAN(EDSRGAN),Sub-Pixel-GAN,SRGAN,and Efficient Wider Activation-B GAN(WDSR-b-GAN).To train the proposed models,we have employed images from the famous BreakHis dataset and enlarged them by 4×and 16×upscale factors with the ground truth of the size of 256×256×***,several evaluation metrics like Peak Signal-to-Noise Ratio(PSNR),Mean Squared Error(MSE),Structural Similarity Index(SSIM),Multiscale Structural Similarity Index(MS-SSIM),and histogram are applied to make comprehensive and objective comparisons to determine the best methods in terms of efficiency,training time,and storage *** obtained results reveal the superiority of the proposed models over traditional and benchmark models in terms of color and texture restoration and detection by achieving an accuracy of 99.7433%.

关键词： GAN medical images SSIM MS-SSIM PSNR SISR

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