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A flexible capacitive photoreceptor for the biomimetic retina

Light(science & Applications) 2022年第1期11卷 38-49页

作者： Mani Teja Vijjapu Mohammed E.Fouda Agamyrat Agambayev Chun Hong Kang Chun-Ho Lin Boon S.Ooi Jr-Hau He Ahmed M.Eltawil Khaled N.Salama Sensors lab Advanced Membranes and Porous Materials CenterComputerElectrical and Mathematical Science and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Kingdom of Saudi Arabia Communication and Computing Systems Lab ComputerElectrical and Mathematical Science and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Kingdom of Saudi Arabia Department of Electrical Engineering and Computer Science University of California-IrvineIrvineCA 92612USA Department of Electrical Computer and Energy EngineeringArizona State UniversityTempeAZUSA Computer Electrical and Mathematical Science and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Kingdom of Saudi Arabia Department of Materials Science and Engineering City University of Hong KongHong Kong SARChina

Neuromorphic vision sensors have been extremely beneficial in developing energy-efficient intelligent systems for robotics and privacy-preserving security *** is a dire need for devices to mimic the retina's photoreceptors that encode the light illumination into a sequence of spikes to develop such ***,we develop a hybrid perovskite-based flexible photoreceptor whose capacitance changes proportionally to the light intensity mimicking the retina's rod cells,paving the way for developing an efficient artificial retina *** proposed device constitutes a hybrid nanocomposite of perovskites(methyl-ammonium lead bromide)and the ferroelectric terpolymer(polyvinylidene fluoride trifluoroethylene-chlorofluoroethylene).A metal-insulator-metal type capacitor with the prepared composite exhibits the unique and photosensitive capacitive behavior at various light intensities in the visible light *** proposed photoreceptor mimics the spectral sensitivity curve of human photopic *** hybrid nanocomposite is stable in ambient air for 129 weeks,with no observable degradation of the composite due to the encapsulation of hybrid perovskites in the hydrophobic *** functionality of the proposed photoreceptor to recognize handwritten digits(MNIST)dataset using an unsupervised trained spiking neural network with 72.05%recognition accuracy is *** demonstration proves the potential of the proposed sensor for neuromorphic vision applications.

关键词： perovskite illumination preserving

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Evaluation of text legibility in alternative imaging approaches to microfiche digitization

Evaluation of text legibility in alternative imaging approac...

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Archiving 2021

作者： Deborah, Hilda Mandai, Dipendra J. Norwegian Colour and Visual Computing Laboratory Department of Computer Science NTNU Norwegian University of Science and Technology Gjovik Norway

ISBN: (纸本)9780892083541

Microfiche was a common format used in microforms reproductions of documents, extensively used for archival storage before the move to digital formats. While contemporary documents are still available for digitization, others from older historical periods are no longer physically accessible for various reasons. In some cases, their microfiche copies are available, making microfiche digitization a must. However, a microfiche reader is not always available and, even then, it is a machine made for the purpose of reading and not for data collection. In this work, the performance two imaging devices are evaluated as alternatives to the traditional microfiche reader, by means of optical character recognition (OCR). Results show that this alternative surpasses the performance of a microfiche reader in terms of text legibility. © 2021 Society for Imaging science and Technology.

关键词： Digital storage

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DreamMesh: Jointly Manipulating and Texturing Triangle Meshes for Text-to-3D Generation

arXiv

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arXiv 2024年

作者： Yang, Haibo Chen, Yang Pan, Yingwei Yao, Ting Chen, Zhineng Wu, Zuxuan Jiang, Yu-Gang Mei, Tao School of Computer Science Fudan University China Shanghai Collaborative Innovation Center of Intelligent Visual Computing China HiDream.ai Inc. China

Learning radiance fields (NeRF) with powerful 2D diffusion models has garnered popularity for text-to-3D generation. Nevertheless, the implicit 3D representations of NeRF lack explicit modeling of meshes and textures over surfaces, and such surface-undefined way may suffer from the issues, e.g., noisy surfaces with ambiguous texture details or cross-view inconsistency. To alleviate this, we present DreamMesh, a novel text-to-3D architecture that pivots on well-defined surfaces (triangle meshes) to generate high-fidelity explicit 3D model. Technically, DreamMesh capitalizes on a distinctive coarse-to-fine scheme. In the coarse stage, the mesh is first deformed by text-guided Jacobians and then DreamMesh textures the mesh with an interlaced use of 2D diffusion models in a tuning free manner from multiple viewpoints. In the fine stage, DreamMesh jointly manipulates the mesh and refines the texture map, leading to high-quality triangle meshes with high-fidelity textured materials. Extensive experiments demonstrate that DreamMesh significantly outperforms state-of-the-art text-to-3D methods in faithfully generating 3D content with richer textual details and enhanced geometry. Our project page is available at https://***. Copyright © 2024, The Authors. All rights reserved.

关键词： Mesh generation

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MAD-ANET:Malware Detection Using Attention-Based Deep Neural Networks

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computer Modeling in Engineering & sciences 2025年第4期143卷 1009-1027页

作者： Waleed Khalid Al-Ghanem Emad Ul Haq Qazi Tanveer Zia Muhammad Hamza Faheem Muhammad Imran Iftikhar Ahmad Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11362Saudi Arabia Centre of Artificial Intelligence Naif Arab University for Security SciencesRiyadh14812Saudi Arabia School of Arts and Sciences The University of Notre DameSydneyNSW 2007Australia Center for Smart Analytics Institute of InnovationScience and SustainabilityFederation University AustraliaBerwickVIC 3806Australia Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

In the current digital era,new technologies are becoming an essential part of our ***,the number ofmalicious software ormalware attacks is rapidly *** is no doubt,themajority ofmalware attacks can be detected by most antivirus ***,such types of antivirus programs are one step behind malicious *** to these dilemmas,deep learning become popular in the detection and classification of malicious ***,researchers have significantly focused on finding solutions for malware attacks by analyzing malicious samples with the help of different techniques and *** this research,we presented a lightweight attention-based novel deep Convolutional Neural Network(DNN-CNN)model for binary and multi-class malware classification,including benign,trojan horse,ransomware,and *** applied the Principal Component Analysis(PCA)technique for feature extraction for binary *** used the Synthetic Minority Oversampling Technique(SMOTE)to handle the imbalanced data during multi-class *** proposed attention-based malware detectionmodel is trained on the benchmarkmalware memory dataset named *** indicate that our model obtained high accuracy for binary and multi-class classification,99.5% and 97.9%,respectively.

关键词： Attention-based CNN malware detection machine learning deep learning classification

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Optimized Phishing Detection with Recurrent Neural Network and Whale Optimizer Algorithm

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computers, Materials & Continua 2024年第9期80卷 4895-4916页

作者： Brij Bhooshan Gupta Akshat Gaurav Razaz Waheeb Attar Varsha Arya Ahmed Alhomoud Kwok Tai Chui Department of Computer Science and Information Engineering Asia UniversityTaichung413Taiwan Symbiosis Centre for Information Technology(SCIT) Symbiosis International UniversityPuneMaharashtra411057India Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India Ronin Institute MontclairNJ 07043USA Management Department College of Business AdministrationPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Business Administration Asia UniversityTaichung413Taiwan Department of Electrical and Computer Engineering Lebanese American UniversityBeirut1102Lebanon Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Electronic Engineering and Computer Science Hong Kong Metropolitan University(HKMU)HomantinHong KongChina

Phishing attacks present a persistent and evolving threat in the cybersecurity land-scape,necessitating the development of more sophisticated detection *** machine learning approaches to phishing detection have relied heavily on feature engineering and have often fallen short in adapting to the dynamically changing patterns of phishingUniformResource Locator(URLs).Addressing these challenge,we introduce a framework that integrates the sequential data processing strengths of a Recurrent Neural Network(RNN)with the hyperparameter optimization prowess of theWhale Optimization Algorithm(WOA).Ourmodel capitalizes on an extensive Kaggle dataset,featuring over 11,000 URLs,each delineated by 30 *** WOA’s hyperparameter optimization enhances the RNN’s performance,evidenced by a meticulous validation *** results,encapsulated in precision,recall,and F1-score metrics,surpass baseline models,achieving an overall accuracy of 92%.This study not only demonstrates the RNN’s proficiency in learning complex patterns but also underscores the WOA’s effectiveness in refining machine learning models for the critical task of phishing detection.

关键词： Phishing detection Recurrent Neural Network(RNN) Whale Optimization Algorithm(WOA) cybersecurity machine learning optimization

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Testing the Unknown: A Framework for OpenMP Testing via Random Program Generation 24

Testing the Unknown: A Framework for OpenMP Testing via Rand...

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Proceedings of the SC '24 Workshops of the International Conference on High Performance computing, Network, Storage, and Analysis

作者： Ignacio Laguna Patrick Chapman Konstantinos Parasyris Giorgis Georgakoudis Cindy Rubio-González Center for Applied Scientific Computing Lawrence Livermore National Laboratory Department of Computer Science University of California Davis

ISBN: (纸本)9798350355543

We present a randomized differential testing approach to test OpenMP implementations. In contrast to previous work that manually creates dozens of verification and validation tests, our approach is able to randomly generate thousands of tests, exposing OpenMP implementations to a wide range of program behaviors. We represent the space of possible random OpenMP tests using a grammar and implement our method as an extension of the Varity program generator. By generating 1,800 OpenMP tests, we find various performance anomalies and correctness issues when we apply them to three OpenMP implementations: GCC, Clang, and Intel. We also present several case studies that analyze the anomalies and give more details about the classes of tests that our approach creates.

关键词： OpenMP

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FedEdge: Accelerating Edge-Assisted Federated Learning 23

FedEdge: Accelerating Edge-Assisted Federated Learning

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2023 World Wide Web Conference, WWW 2023

作者： Wang, Kaibin He, Qiang Chen, Feifei Jin, Hai Yang, Yun School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing Technologies Swinburne University of Technology Australia School of Information Technology Deakin University Australia National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781450394161

Federated learning (FL) has been widely acknowledged as a promising solution to training machine learning (ML) model training with privacy preservation. To reduce the traffic overheads incurred by FL systems, edge servers have been included between clients and the parameter server to aggregate clients' local models. Recent studies on this edge-assisted hierarchical FL scheme have focused on ensuring or accelerating model convergence by coping with various factors, e.g., uncertain network conditions, unreliable clients, heterogeneous compute resources, etc. This paper presents our three new discoveries of the edge-assisted hierarchical FL scheme: 1) it wastes significant time during its two-phase training rounds;2) it does not recognize or utilize model diversity when producing a global model;and 3) it is vulnerable to model poisoning attacks. To overcome these drawbacks, we propose FedEdge, a novel edge-assisted hierarchical FL scheme that accelerates model training with asynchronous local federated training and adaptive model aggregation. Extensive experiments are conducted on two widely-used public datasets. The results demonstrate that, compared with state-of-the-art FL schemes, FedEdge accelerates model convergence by 1.14 × -3.20 ×, and improves model accuracy by 2.14% - 6.63%. © 2023 ACM.

关键词： Edge computing

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DuMo: Dual Encoder Modulation Network for Precise Concept Erasure

arXiv

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arXiv 2025年

作者： Han, Feng Chen, Kai Gong, Chao Wei, Zhipeng Chen, Jingjing Jiang, Yu-Gang Shanghai Key Lab of Intell. Info. Processing School of Computer Science Fudan University China Shanghai Collaborative Innovation Center on Intelligent Visual Computing China

The exceptional generative capability of text-to-image models has raised substantial safety concerns regarding the generation of Not-Safe-For-Work (NSFW) content and potential copyright infringement. To address these concerns, previous methods safeguard the models by eliminating inappropriate concepts. Nonetheless, these models alter the parameters of the backbone network and exert considerable influences on the structural (low-frequency) components of the image, which undermines the model’s ability to retain non-target concepts. In this work, we propose our Dual encoder Modulation network (DuMo), which achieves precise erasure of inappropriate target concepts with minimum impairment to non-target concepts. In contrast to previous methods, DuMo employs the Eraser with PRior Knowledge (EPR) module which modifies the skip connection features of the U-NET and primarily achieves concept erasure on details (high-frequency) components of the image. To minimize the damage to non-target concepts during erasure, the parameters of the backbone U-NET are frozen and the prior knowledge from the original skip connection features is introduced to the erasure process. Meanwhile, the phenomenon is observed that distinct erasing preferences for the image structure and details are demonstrated by the EPR at different timesteps and layers. Therefore, we adopt a novel Time-Layer MOdulation process (TLMO) that adjusts the erasure scale of EPR module’s outputs across different layers and timesteps, automatically balancing the erasure effects and model’s generative ability. Our method achieves state-of-the-art performance on Explicit Content Erasure (detecting only 34 nude parts), Cartoon Concept Removal (with an average LPIPSda of 0.428, 0.113 higher than SOTA at 0.315), and Artistic Style Erasure (with an average LPIPSda of 0.387, 0.088 higher than SOTA at 0.299), clearly outperforming alternative methods. Code is available at https://***/Maplebb/DuMo Copyright © 2025, The Author

关键词： HTTP

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Convolutional Neural Network Based Intelligent Handwritten Document Recognition

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computers, Materials & Continua 2022年第3期70卷 4563-4581页

作者： Sagheer Abbas Yousef Alhwaiti Areej Fatima Muhammad A.Khan Muhammad Adnan Khan Taher M.Ghazal Asma Kanwal Munir Ahmad Nouh Sabri Elmitwally School of Computer Science National College of Business administration and EconomicsLahore54000Pakistan College of Computer and Information Sciences Jouf UniversitySakaka72341Saudi Arabia Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13557Korea Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan Malaysia(UKM)Bangi 43600SelangorMalaysia School of Information Technology Skyline University CollegeUniversity City Sharjah1797SharjahUAE Department of Computer Science GC UniversityLahorePakistan Department of Computer Science Faculty of Computers and Artificial IntelligenceCairo University12613Egypt

This paper presents a handwritten document recognition system based on the convolutional neural network *** today’s world,handwritten document recognition is rapidly attaining the attention of researchers due to its promising behavior as assisting technology for visually impaired *** technology is also helpful for the automatic data entry *** the proposed systemprepared a dataset of English language handwritten character *** proposed system has been trained for the large set of sample data and tested on the sample images of user-defined handwritten *** this research,multiple experiments get very worthy recognition *** proposed systemwill first performimage pre-processing stages to prepare data for training using a convolutional neural *** this processing,the input document is segmented using line,word and character *** proposed system get the accuracy during the character segmentation up to 86%.Then these segmented characters are sent to a convolutional neural network for their *** recognition and segmentation technique proposed in this paper is providing the most acceptable accurate results on a given *** proposed work approaches to the accuracy of the result during convolutional neural network training up to 93%,and for validation that accuracy slightly decreases with 90.42%.

关键词： Convolutional neural network segmentation skew cursive characters recognition

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Federated Learning for Enhanced Intrusion Detection in Smart City Environments

Federated Learning for Enhanced Intrusion Detection in Smart...

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International Conference on Open Source Systems and Technologies (ICOSST)

作者： Soomaiya Hamid Narmeen Zakaria Bawany Center for Computing Research Department of Computer Science and Software Engineering Jinnah University for Women Karachi Pakistan

ISBN: (数字)9798331508692

ISBN: (纸本)9798331508708

The integration of IoT devices in smart cities enhances urban infrastructure, services, and governance but also introduces significant cybersecurity challenges. Traditional centralized Intrusion Detection Systems (IDS) face several issues, including data privacy concerns and high-power consumption due to centralized data processing. These challenges increase the risks of unauthorized access, data breaches, and privacy violations, undermining user trust and compliance with privacy regulations. Additionally, the centralization of data and processing leads to higher power consumption, making these systems less sustainable for widespread deployment in smart cities. This research addresses these issues by proposing a Federated Learning (FL)based intrusion detection framework for smart cities. FL enables collaborative and privacy-preserving model training across distributed IoT devices, mitigating the need to share sensitive data centrally. By aggregating local model updates, FL ensures data privacy and distributes the computational workload, significantly reducing power consumption compared to traditional centralized systems. The proposed model leverages advanced AI techniques and is trained using the IoTID20 dataset. The Flower framework, utilizing the FedAvg algorithm, facilitates the federated learning process. Our experimental results demonstrate that the global model achieves 98% accuracy, with individual clients achieving accuracies of around 85% to 98%. This approach provides continuous learning mechanisms, anomaly detection, and ensemble learning capabilities, enhancing the resilience of federated intrusion detection systems against emerging threats and adversarial attacks. This research systematically investigates the application of federated learning for intrusion detection in smart city networks, addressing key challenges and advancing the state-of-the-art in decentralized cybersecurity solutions. The proposed framework offers a robust, scalable, and privacyco

关键词： Data privacy Power demand Accuracy Smart cities Federated learning Computational modeling Intrusion detection Data models Internet of Things computer security

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