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Masked Generative Light Field Prompting for Pixel-Level Structure Segmentations

Research 2024年第4期2024卷 533-544页

作者： Mianzhao Wang Fan Shi Xu Cheng Shengyong Chen The Engineering Research Center of Learning-Based Intelligent System(Ministry of Education) Tianjin University of TechnologyTianjin 300384China Key Laboratory of Computer Vision and System(Ministry of Education) Tianjin University of TechnologyTianjin 300384China School of Computer Science and Engineering Tianjin University of TechnologyTianjin 300384China

Pixel-level structure segmentations have attracted considerable attention,playing a crucial role in autonomous driving within the metaverse and enhancing comprehension in light field-based machine ***,current light field modeling methods fail to integrate appearance and geometric structural information into a coherent semantic space,thereby limiting the capability of light field transmission for visual *** this paper,we propose a general light field modeling method for pixel-level structure segmentation,comprising a generative light field prompting encoder(LF-GPE)and a prompt-based masked light field pretraining(LF-PMP)*** LF-GPE,serving as a light field backbone,can extract both appearance and geometric structural cues *** aligns these features into a unified visual space,facilitating semantic ***,our LF-PMP,during the pretraining phase,integrates a mixed light field and a multi-view light field *** prioritizes considering the geometric structural properties of the light field,enabling the light field backbone to accumulate a wealth of prior *** evaluate our pretrained LF-GPE on two downstream tasks:light field salient object detection and semantic *** results demonstrate that LF-GPE can effectively learn high-quality light field features and achieve highly competitive performance in pixel-level segmentation tasks.

关键词： prompt backbone integrate

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Enhancing Voice Phishing Detection Using Multilingual Back-Translation and SMOTE: An Empirical Study

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IEEE Access 2025年 13卷 37946-37965页

作者： Boussougou, Milandu Keith Moussavou Hamandawana, Prince Park, Dong-Joo Soongsil University Department of Computer Science and Engineering Seoul06978 Korea Republic of Ajou University Department of Software and Computer Engineering Suwon16499 Korea Republic of Soongsil University School of Computer Science and Engineering Seoul06978 Korea Republic of

With the widespread global trend of voice phishing or vishing attacks, the development of effective detection models using artificial intelligence (AI) has been hindered by the lack of high-quality and large volumes of data. This lack of data reflecting a real vishing scenario often leads to imbalanced datasets and biased detection models. Therefore, we present in this paper a data augmentation (DA) method for expanding the imbalanced Korean call content vishing (KorCCVi) dataset to address the existing data asymmetry problem and enhance the performance of Korean vishing detection. The proposed approach for DA involves using the back-translation (BT) method with three different intermediate languages: English, Chinese, and Japanese. The proposed method offers several advantages over the traditional synthetic minority oversampling technique (SMOTE), which is the main technique used to compare with our multilingual BT approach. Using these two DA techniques, several machine learning (ML) and deep learning (DL) models were trained on the original imbalanced dataset, the dataset balanced with SMOTE and its variants, and the dataset augmented with our method. We analyzed the impact of these DA methods on the performance of the models, demonstrated the benefits of each approach, and suggested the most suitable approach. The performance of the trained models was evaluated using the accuracy, precision, recall, and F1-score metrics. The experimental results demonstrated that the proposed multilingual BT method effectively expands the dataset while preserving its contextual and linguistic characteristics. The average performance of the models revealed that those trained on the augmented dataset outperformed the other models. They achieved F1-scores of 98.91% for the back-translated data, 98.14% for the original data, and 97.23% for SMOTE. © 2025 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecomm

关键词： Phishing

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Reconstruction Attacks in Template-Based ECG Biometric Recognition systems

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IEEE Internet of Things Journal 2024年第8期11卷 14971-14984页

作者： Lee, Song-Hong Nien, Cing-Ping Wu, Shun-Chi Swindlehurst, A. Lee National Tsing Hua University Department of Engineering and System Science Hsinchu30013 Taiwan University of California Department of Electrical Engineering and Computer Science IrvineCA92697 United States

The success of Internet of Things (IoT) services will be determined by how the security of the IoT devices and the networks to which they are connected can be guaranteed. Integrating biometric recognition technology into IoT systems has gained popularity as a means of accomplishing this objective. Electrocardiograms (ECGs) have become a promising biometric tool for security because their intrinsic and dynamic nature makes them difficult to steal and forge for replay attacks. However, as with other biometric-based security approaches, attacks on ECG biometric systems have been developed. This study examines the vulnerability of template-based ECG biometric systems to reconstruction attacks. These attacks involve exposing the biometric templates of registered subjects in a hacked database and attempting to reconstruct their ECGs to spoof the system. Both deep learning models and 'heuristic' approaches are used to perform this task, depending on the intruder's level of knowledge of the template construction. Several reconstruction attack strategies are proposed and evaluated for fiducial- and PCA-based systems using ECGs from the Physikalisch-Technische Bundesanstalt database of 285 subjects. The experimental results demonstrate a reconstruction similarity of at least 0.93 and an increase in the false-positive identification-error rate of more than 73%. © 2014 IEEE.

关键词： Electrocardiograms

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Artificial intelligence algorithms for object detection and recognition in video and images

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Multimedia Tools and Applications 2025年 1-18页

作者： Dakshinamoorthy, Prabakar Rajaram, Gnanajeyaraman garg, Shruti Murugan, Prabhu Manimaran, A. Sundar, Ramesh Department of Data Science and Business System School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur Chennai India Department of Computer Science Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Birla Institute of Technology Mesra Ranchi India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India Department of Computer Science Engineering Saveetha School Of EngineeringSaveetha Institute of Medical and Technical Sciences Chennai6021055 India Department of Netwoking and Communication School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur Chennai India

The usage of machine learning and deep learning algorithms have necessitated Artificial Intelligence'. AI is aimed at automating things by limiting human interference. It is widely used in IT, healthcare, finance, and agriculture. It is achieved through several deep learning algorithms that reflect the human brain's intelligence. These AI algorithms can be manipulated according to changing needs and improved efficiency. This paper tries to utilize the developments made in AI technology to classify the images and recognize the objects present in them. One widely used AI algorithm is CNN (Convolutional Neural Networks). The CNN is a deep learning-based algorithm that consists of various layers that extract and filters the parameters present in the images. Some additional layers of ResNet50 and the CNN algorithm are used to extract the parameters to improve image recognition accuracy. The image dataset taken for training and testing the proposed model is imageNet. The images are initially processed before sending them to the proposed model. The proposed model is trained, validated, and tested through the images obtained after the initial processing. The same process is repeated several times until getting the maximum accuracy. The accuracy of the proposed model in terms of image recognition is recorded. The obtained results are compared with other image classification algorithms like VGG16 and VGG19. It is concluded that the proposed model outperforms other traditional methods in terms of accuracy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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An Adaptive Scalable Data Pipeline for Multiclass Attack Classification in Large-Scale IoT Networks

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Big Data Mining and Analytics 2024年第2期7卷 500-511页

作者： Selvam Saravanan Uma Maheswari Balasubramanian Department of Computer Science and Engineering Amrita School of ComputingAmrita Vishwa VidyapeethamBengaluru 560035India

The current large-scale Internet of Things(IoT)networks typically generate high-velocity network traffic *** use IoT devices to create botnets and launch attacks,such as DDoS,Spamming,Cryptocurrency mining,Phishing,*** service providers of large-scale IoT networks need to set up a data pipeline to collect the vast network traffic data from the IoT devices,store it,analyze it,and report the malicious IoT devices and types of ***,the attacks originating from IoT devices are dynamic,as attackers launch one kind of attack at one time and another kind of attack at another *** number of attacks and benign instances also vary from time to *** phenomenon of change in attack patterns is called concept ***,the attack detection system must learn continuously from the ever-changing real-time attack patterns in large-scale IoT network *** meet this requirement,in this work,we propose a data pipeline with Apache Kafka,Apache Spark structured streaming,and MongoDB that can adapt to the ever-changing attack patterns in real time and classify attacks in large-scale IoT *** concept drift is detected,the proposed system retrains the classifier with the instances that cause the drift and a representative subsample instances from the previous training of the *** proposed approach is evaluated with the latest dataset,IoT23,which consists of benign and several attack instances from various IoT *** classification accuracy is improved from 97.8%to 99.46%by the proposed *** training time of distributed random forest algorithm is also studied by varying the number of cores in Apache Spark environment.

关键词： Internet of Things(IoT) concept drift Apache Spark MongoDB Apache Kafka streaming

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CLIP-Flow:Decoding images encoded in CLIP space

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Computational Visual Media 2024年第6期10卷 1157-1168页

作者： Hao Ma Ming Li Jingyuan Yang Or Patashnik Dani Lischinski Daniel Cohen-Or Hui Huang Visual Computing Research Center College of Computer Science and Software EngineeringShenzhen UniversityShenzhen 518060China Department of Computer Science Tel Aviv UniversityTel Aviv 6997801Israel School of Computer Science and Engineering the Hebrew University of JerusalemJerusalem 91904Israel

This study introduces CLIP-Flow,a novel network for generating images from a given image or *** effectively utilize the rich semantics contained in both modalities,we designed a semantics-guided methodology for image-and text-to-image *** particular,we adopted Contrastive Language-Image Pretraining(CLIP)as an encoder to extract semantics and StyleGAN as a decoder to generate images from such ***,to bridge the embedding space of CLIP and latent space of StyleGAN,real NVP is employed and modified with activation normalization and invertible *** the images and text in CLIP share the same representation space,text prompts can be fed directly into CLIP-Flow to achieve text-to-image *** conducted extensive experiments on several datasets to validate the effectiveness of the proposed image-to-image synthesis *** addition,we tested on the public dataset Multi-Modal CelebA-HQ,for text-to-image *** validated that our approach can generate high-quality text-matching images,and is comparable with state-of-the-art methods,both qualitatively and quantitatively.

关键词： image-to-image text-to-image contrastive language-image pretraining(CLIP) flow StyleGAN

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Unleashing the potential of ChatGPT in education: Revolutionizing learning and collaboration

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IEEE Potentials 2024年第6期43卷 13-22页

作者： Chandrasekaran, Balaji Nair, Smrithy Girijakumari Sreekantan College of Engineering Guindy Anna University Department of Computer Science and Engineering Chennai600025 India Vellore Institute of Technology School of Computer Science and Engineering Chennai600127 India

ChatGPT, an advanced language model powered by artificial intelligence, has emerged as a transformative tool in the field of education. This article explores the potential of ChatGPT in revolutionizing learning and collaboration within educational settings. By leveraging natural language processing and machine learning, ChatGPT offers an interactive and personalized learning experience, enabling students to engage in dynamic conversations and receive instant feedback. Moreover, ChatGPT may facilitate collaborative learning by promoting effective communication and knowledge sharing among students and educators. This article examines the various applications of ChatGPT in education, including virtual tutoring, adaptive learning, and intelligent chatbots. It also discusses the benefits and considerations associated with implementing ChatGPT, such as ethical considerations, data privacy, and potential biases. While acknowledging the limitations and challenges, this article emphasizes the promising potential of ChatGPT in transforming education by enhancing student engagement, fostering critical thinking skills, and expanding access to quality education. © 1988-2012 IEEE.

关键词： Adversarial machine learning

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A significant wave height prediction method with ocean characteristics fusion and spatiotemporal dynamic graph modeling

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Acta Oceanologica Sinica 2024年第12期43卷 13-33页

作者： Xiao Yin Taoxing Wu Jie Yu Xiaoyu He Lingyu Xu Department of Computer Engineering and Science Shanghai UniversityShanghai 200444China School of Computer Science and Technology Zhejiang Sci-Tech UniversityHangzhou 310018China

Accurate significant wave height(SWH)prediction is essential for the development and utilization of wave *** learning methods such as recurrent and convolutional neural networks have achieved good results in SWH ***,these methods do not adapt well to dynamic seasonal variations in wave *** this study,we propose a novel method—the spatiotemporal dynamic graph(STDG)neural *** method predicts the SWH of multiple nodes based on dynamic graph modeling and multi-characteristic ***,considering the dynamic seasonal variations in the wave direction over time,the network models wave dynamic spatial dependencies from long-and short-term pattern ***,to correlate multiple characteristics with SWH,the network introduces a cross-characteristic transformer to effectively fuse multiple ***,we conducted experiments on two datasets from the South China Sea and East China Sea to validate the proposed method and compared it with five prediction methods in the three *** experimental results show that the proposed method achieves the best performance at all predictive scales and has greater advantages for extreme value ***,an analysis of the dynamic graph shows that the proposed method captures the seasonal variation mechanism of the waves.

关键词： significant wave height forecasting dynamic seasonal variation dynamic graph neural networks

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A Novel Enhanced Approach for Security and Privacy Preserving in IoT Devices with Federal Learning Technique

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SN computer science 2024年第6期5卷 750页

作者： Moeed, Syed Abdul Karnati, Ramesh Ashmitha, G. Mohammad, Gouse Baig Mohanty, Sachi Nandan Department of Computer Science and Engineering Kakatiya Institute of Technology and Science Department of Computer Science and Engineering Vardhaman College of Engineering School of Computer Science & amp Engineering (SCOPE) VIT-AP University

The Internet of Things (IoT) has revolutionized our lives, but it has also introduced significant security and privacy challenges. The vast amount of data collected by these devices, often containing sensitive information, makes them prime targets for cyberattacks. Traditional security methods struggle to keep pace with the evolving threat landscape of the interconnected IoT world. In response, this paper explores the transformative potential of federated learning (FL) in safeguarding both the privacy and security of IoT devices. FL keeps data on individual devices, only sharing updated models, not raw data, thus protecting user privacy and eliminating the need for a central data storage server, which reduces the risk of data breaches and security vulnerabilities. The effectiveness of the proposed model is evaluated using established open-source datasets like NSL-KDD and UNSW NB15, ensuring real-world applicability. Analysis of the dataset's features enabled the development of a model utilizing federated learning. Notably, the proposed model achieved superior performance with FL in detecting attacks on IoT networks. Furthermore, this research investigates the transformative potential of FL to address the inherent security and privacy challenges plaguing traditional, centralized data collection methods in the ever-expanding realm of IoT devices. FL empowers collaborative learning on distributed datasets, allowing devices to collectively improve security without compromising user privacy. The findings of this study demonstrate the promise of FL as a secure and privacy-preserving solution for the future of IoT. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cyberattacks Federal learning Internet of Things Machine learning Security and privacy

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IDBNWP: Improved deep belief network for workload prediction: Hybrid optimization for load balancing in cloud system

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Multimedia Tools and Applications 2024年第16期84卷 1-19页

作者： Ajil, A. Kumar, E. Saravana Department of Computer Science and Engineering The Oxford College of Engineering Visvesvaraya Technological University Karnataka Belagavi India School of Computer Science and Engineering REVA University Karnataka Bengaluru India

The achievement of cloud environment is determined by the efficiency of its load balancing with proper allocation of resources. The proactive forecasting of future workload, accompanied by the allocation of resources, has emerged as a primary method for addressing other inbuilt problems, such as the underneath or over utilization of physical machines, resource wastage, VM migration, Quality-of-Services (QoS) violations, load balancing, and so on. In this paper, we have introduced a novel workload prediction and load balancing approach which includes two major phases like workload prediction with deep learning and optimal load balancing. In the initial workload prediction stage, we have proposed an Improved Deep Belief Network (IDBN), which efficiently predict the load as under load, overload or equally balanced. Afterwards, the load gets balanced by the utilization of the hybrid optimization named Bald Eagle Assisted Butterfly Optimization Algorithm (BEABOA), which consider the constraints like makespan (70), communication cost (4000), response time (1.1), turnaround time (2), migration cost (0.1) during the process of optimal load balancing. Also, the outcomes demonstrate that this proposed workload prediction and load balancing approach can offer superior outcomes. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Resource allocation

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