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2021 International Conference on Culture-Oriented Science and Technology, ICCST 2021

作者： Tu, Zhongwen Liu, Bin Zhao, Wei Cao, Baoyu Communication University of China Broadcasting and Anchoring School Beijing China Communication University of China School of Information and Communication Engineering Beijing China Communication University of China School of Data Science and Intelligent Media Beijing China Communication University of China School of Computer and Cyber Science Beijing China

ISBN: (纸本)9781665442541

In order to provide an effective and available database for speech emotion recognition and research on broadcast speech, an speech emotion database based on broadcast vocalization method is established, which contains seven discrete emotions, namely, happy, very happy, sad, very sad, angry, very angry and neutral. The database is recorded by four graduate students (two male and two female) from Communication University of China, who majoring in broadcasting and anchoring, at the professional recording room in a performance way. To prove the effectiveness of the database, an evaluation group of 10 people, both professional and nonprofessional, was organized to conduct a subjective evaluation experiment on the randomly selected samples from the database. The experiment results show that this database is an effective and reliable Chinese speech emotion database of broadcasting. © 2021 IEEE.

关键词： database systems

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Enhancing IoT Network Security through AIDriven Intrusion Detection with Hybrid AutoencoderGAN Fusion 23

Enhancing IoT Network Security through AIDriven Intrusion De...

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Proceedings of the 5th International Conference on Information Management & Machine Intelligence

作者： Rupali A. Mahajan Sukhvinder Singh Dari Ritika Dhabliya Gabriela Michael Anil Kumar Nidhi Ranjan Computer Associate Professor COMP VIIT Pune Maharashtra India Computer Associate Professor Symbiosis Law School Nagpur Campus Symbiosis International (Deemed University) Pun India Computer Director Yashika Journal Publications Pvt. Limited Wardha Maharashtra India India Computer Symbiosis Law School Nagpur Campus Symbiosis International (Deemed University) Pune India India Department of Artificial Intelligence & Data Science Professor Poornima Institute Of Engineering &Technology Jaipur Rajasthan India India Computer Associate Professor Vasantdada Patil Pratishthan?s College of engineering and Visual Arts Mumbai Maharashtra India India

ISBN: (纸本)9798400709418

The rapid growth of Internet of Things (IoT)IoT devices has changed how we connect and interact with our environment. This advancement has also complicated network security. As IoT networks grow, data integrity and confidentiality become more important. This study integrates a hybrid autoencoder and Generative Adversarial Network (GAN) model to use AI to solve security problems. The UNSW-NB15 dataset, a benchmark for network IDSs, is used to rigorously evaluate the proposed model. An autoencoder and GAN combination offers promising IDS for IoT networks. The autoencoder component excels at capturing concise and illustrative data representations, enabling it to spot network anomalies. The GAN component improves the model's ability to generate synthetic data samples that closely resemble network traffic patterns, making it easier to identify complex and novel attacks. The UNSW-NB15 dataset is known for its extensive network traffic data, making it ideal for testing the proposed artificial intelligence-based IDS. The proposed hybrid model achieves 99.13% accuracy. The result outperforms traditional DL methods. The proposed model's increased precision shows its ability to detect and address network anomaly risks. The proposed system is evaluated using precision, recall, F1-Score, AUC-ROC, MCC, and Cohen's Kappa. The thorough evaluation shows that the Proposed hybrid autoencoder-GAN model provides a complete and efficient IoT network security solution in all dimensions. In conclusion, artificial intelligence-based intrusion detection and a hybrid model that combines autoencoder and generative adversarial network techniques improve IoT network security. The model's UNSW-NB15 dataset accuracy shows its ability to protect IoT ecosystems from many threats. This study helps improve the reliability and robustness of networks, boosting confidence in the growing world of interconnected devices.

关键词： Deep Learning

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Deep Learning Driven - Pest Classification and Sustainable Agriculture

Deep Learning Driven - Pest Classification and Sustainable A...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： M Sirish Kumar R Usha B Rama Kantha Reddy S Ramachandra P Jaya Prakash B Swetha Dept. of Data Science School of Computing Mohan Babu University India CSE Madanapalle Institute of Technology and Science Madanapalle CSE (AI and ML) Sri Venkateswara College of Engineering Tirupati CSE Mother Theresa Institute of Engineering and Technology Palamaner Department of IT Sri Venkateswara College of Engineering Tirupati Dept. of Master of Computer Applications Mohan Babu University India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

This study introduces a system designed to identify pests in crops and classify them as either beneficial or harmful. The project begins by providing a comprehensive overview of various pest identification methods, analyzing their strengths and limitations. To address the problem, a categorization approach is proposed that incorporates convolutional neural networks (CNN) to build the pest identification and classification model. The model was trained using a dataset containing images of different pest species. Extensive testing was conducted on a large dataset, and the system's performance was validated against multiple traditional classification models. The proposed framework achieved a classification accuracy of 90%, outperforming conventional methods. This research highlights agriculture's vital role in the economy and underscores the potential of advanced technologies in supporting sustainable farming practices.

关键词： Deep learning Accuracy Insects System performance Transfer learning Crops Pesticides Agriculture Convolutional neural networks Testing

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ACDMSR: Accelerated Conditional Diffusion Models for Single Image Super-Resolution

arXiv

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arXiv 2023年

作者： Niu, Axi Trung, Pham Xuan Zhang, Kang Sun, Jinqiu Zhu, Yu Kweon, In So Zhang, Yanning School of Computer Science Northwestern Polytechnical University Xi’an710072 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi’an710072 China School of Electrical Engineering Korea Advanced Institute of Science and Technology Korea Republic of School of Astronautics Northwestern Polytechnical University Xi’an710072 China

Diffusion models have gained significant popularity in the field of image-to-image translation. Previous efforts applying diffusion models to image super-resolution (SR) have demonstrated that iteratively refining pure Gaussian noise using a U-Net architecture trained on denoising at various noise levels can yield satisfactory high-resolution images from low-resolution inputs. However, this iterative refinement process comes with the drawback of low inference speed, which strongly limits its applications. To speed up inference and further enhance the performance, our research revisits diffusion models in image super-resolution and proposes a straightforward yet significant diffusion model-based super-resolution method called ACDMSR (accelerated conditional diffusion model for image super-resolution). Specifically, our method adapts the standard diffusion model to perform super-resolution through a deterministic iterative denoising process. Our study also highlights the effectiveness of using a pre-trained SR model to provide the conditional image of the given low-resolution (LR) image to achieve superior high-resolution results. We demonstrate that our method surpasses previous attempts in qualitative and quantitative results through extensive experiments conducted on benchmark datasets such as Set5, Set14, Urban100, BSD100, and Manga109. Moreover, our approach generates more visually realistic counterparts for low-resolution images, emphasizing its effectiveness in practical scenarios. © 2023, CC BY.

关键词： Image enhancement

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ZETA: ZEro-Trust Attack Framework with Split Learning for Autonomous Vehicles in 6G Networks

ZETA: ZEro-Trust Attack Framework with Split Learning for Au...

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IEEE Conference on Wireless Communications and Networking

作者： Sunder Ali Khowaja Parus Khuwaja Kapal Dev Keshav Singh Lewis Nkenyereye Dan Kilper Faculty of Computing Digital and Data Technological University Dublin Ireland Institute of Business Administration University of Sindh Pakistan Department of Computer Science CONNECT Centre Munster Technological University Ireland Institute of Communications Engineering (ICE) National Sun Yat-sen University Taiwan Department of Computer and Information Security Sejong University South Korea CONNECT Centre School of Engineering Trinity College Dublin Ireland

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

In past, due to data and model security concerns, modern communication systems mainly focus on the use of edge computing devices for enabling immersive applications and services. Federated learning is one of the preferred solutions but it stresses the computation capability of the edge devices for immersive applications. Much research is now focusing on split learning as an alternative due to its ability of performing joint training with limited computing resources. However, split learning is also vulnerable to data reconstruction, feature space hijacking, and model inversion attacks, which are quite common concerning immersive applications such as Metaverse. In this regard, we propose a ZEro-Trust Attack (ZETA) framework for data reconstruction and model inversion attacks for autonomous vehicles opting for split learning strategies. We propose the joint training of client, server, and shadow models for both the reconstruction and main task to fool existing methods. Our experimental results demonstrate that the proposed method is capable of reconstructing client's data with an error of 0.0032. This study is proposed as a basis to design more sophisticated defense mechanisms for autonomous vehicles to protect user services in 5G/6G networks.

关键词： Training Performance evaluation Sensitivity analysis Computational modeling data models Zero Trust Servers

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Communication-efficient distributed covariance sketch, with application to distributed PCA

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2021年第1期22卷 3643-3680页

作者： Zengfeng Huang Xuemin Lin Wenjie Zhang Ying Zhang School of Data Science Fudan University Shanghai China School of Computer Science and Engineering The University of New South Wales Sydney Australia School of Computer Science University of Technology Sydney Sydney Australia

A sketch of a large data set captures vital properties of the original data while typically occupying much less space. In this paper, we consider the problem of computing a sketch of a massive data matrix A ε ℝn × d that is distributed across s machines. Our goal is to output a matrix B ε ℝℓ × d which is significantly smaller than but still approximates A well in terms of covariance error, i.e., ∥AT A — BT B∥2. Such a matrix B is called a covariance sketch of A. We are mainly focused on minimizing the communication cost, which is arguably the most valuable resource in distributed computations. We show that there is a nontrivial gap between deterministic and randomized communication complexity for computing a covariance sketch. More specifically, we first prove an almost tight deterministic communication lower bound, then provide a new randomized algorithm with communication cost smaller than the deterministic lower bound. Based on a well-known connection between covariance sketch and approximate principle component analysis, we obtain better communication bounds for the distributed PCA problem. Moreover, we also give an improved distributed PCA algorithm for sparse input matrices, which uses our distributed sketching algorithm as a key building block.

关键词： matrix sketching PCA distributed streaming low rank approximation communication complexity

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RAST: A Reward Augmented Model for Fine-Grained Sentiment Transfer 10th

RAST: A Reward Augmented Model for Fine-Grained Sentiment Tr...

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10th CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2021

作者： Hu, Xiaoxuan Zhang, Hengtong Zhao, Wayne Xin Li, Yaliang Gao, Jing Wen, Ji-Rong School of Information Renmin University of China Beijing China School of Electrical and Computer Engineering Purdue University West Lafayette United States Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Department of Computer Science and Engineering University at Buffalo Buffalo United States Alibaba Group Hangzhou China

ISBN: (纸本)9783030884796

In this paper, we propose a novel model RAST (Reward Augmented Sentiment Transfer) for fine-grained sentiment transfer. Existing methods usually suffer from two major drawbacks, i.e., blurre d sentiment distinction and unsatisfactory content preservation. Considering the above issues, we design two kinds of rewards to better control sentiment and content. Specially, we develop a pairwise comparative discriminator that enforces to generate sentences with clear distinctions for different sentiment intensities. Moreover, we utilize an effective sampling strategy to obtain pseudo-parallel sentences with minor changes on the input sentence to enhance content preservation. Experiments on a benchmark dataset show that the proposed model outperforms several competitive approaches. © 2021, Springer Nature Switzerland AG.

关键词： computers

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Learning from Multi-Perception Features for Real-Word Image Super-resolution

arXiv

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arXiv 2023年

作者： Niu, Axi Zhang, Kang Pham, Trung X. Wang, Pei Sun, Jinqiu Kweon, In So Zhang, Yanning The School of Computer Science Northwestern Polytechnical University Xi’an710072 China The National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi’an710072 China The School of Electrical Engineering Korea Advanced Institute of Science and Technology Korea Republic of The School of Astronautics Northwestern Polytechnical University Xi’an710072 China

Currently, there are two popular approaches for addressing real-world image super-resolution problems: degradation-estimation-based and blind-based methods. However, degradation-estimation-based methods may be inaccurate in estimating the degradation, making them less applicable to real-world LR images. On the other hand, blind-based methods are often limited by their fixed single perception information, which hinders their ability to handle diverse perceptual characteristics. To overcome this limitation, we propose a novel SR method called MPF-Net that leverages multiple perceptual features of input images. Our method incorporates a Multi-Perception Feature Extraction (MPFE) module to extract diverse perceptual information and a series of newly-designed Cross-Perception Blocks (CPB) to combine this information for effective super-resolution reconstruction. Additionally, we introduce a contrastive regularization term (CR) that improves the model’s learning capability by using newly generated HR and LR images as positive and negative samples for ground truth HR. Experimental results on challenging real-world SR datasets demonstrate that our approach significantly outperforms existing state-of-the-art methods in both qualitative and quantitative measures. © 2023, CC BY.

关键词： Feature extraction

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Enhancing Dental Caries Identification with Deep Learning: A Study of Convolutional Neural Networks and Transfer Learning Approaches

SSRN

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SSRN 2024年

作者： Mannan, Fayqa Adeel, Muhammad Arif, Erssa Li, Jianqiang Ullah, Amin Nawazish, Samra Mahmood, Tariq Rehman, Amjad Shoukat, Ijaz Faculty of Information Technology Beijing University of Technology Beijing100124 China Department of Computer Science Government College University Faisalabad38000 Pakistan Department of Computer Science School of Science National Textile University Faisalabad37610 Pakistan Department of Computer Science Faculty of Computing Riphah International University Faisalabad38000 Pakistan Beijing Engineering Research Center for IoT Software and Systems 100124 China Department of Computer Science Faculty of IT and Computer Science University of Central Punjab Lahore Pakistan School of Computer Science and Engineering Southeast University Nanjing China Software Engineering Department University of Engineering and Technology Taxila Pakistan Artificial Intelligence & Data Analytics Lab CCIS Prince Sultan University Riyadh11586 Saudi Arabia Faculty of information Sciences University of Education Vehari Campus Vehari61100 Pakistan

Various deep learning techniques have been employed to diagnose dental caries using X-ray images. In this study, we utilized deep learning models, including Convolutional Neural Networks (CNNs) and transfer learning models such as Visual Geometry Group (VGG16), VGG19, ResNet50, and Inception V3, to identify dental caries in periapical radiographs. The traditional CNN model comprises three sets of 2D convolutional layers, followed by an activation layer, a max-pooling layer, a flatten layer, one dense layer, another activation layer, dropout, another dense layer, and a final activation layer. For the VGG models, ResNet50, and Inception V3, the top convolutional layers were frozen to prevent retraining of the model, with only the last layers trained to produce the required output. The accuracy achieved by CNN, VGG16, VGG19, ResNet50, and Inception V3 was 90%, 96%, 73%, 70%, and 73%, respectively. VGG16 exhibited the highest accuracy among the models. Hyperparameters were carefully selected using a grid search approach, and the results were validated using the Shuffle-Split-Cross (SSC) validation method. These findings underscore the effectiveness of transfer learning in analyzing medical images, particularly in the diagnosis of dental caries. © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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End-to-End Congestion Avoidance Scheme for Underwater High Speed Communication Networks

End-to-End Congestion Avoidance Scheme for Underwater High S...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Shivank Romil Jain Reena R Prema S. Kadam Sunanda Das Swati Singh Department of Computer Science & Engineering Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune INDIA Computer Science and Engineering Faculty of Engineering and Technology Bangalore Karnataka India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

The cease-to-give up Congestion Avoidance Scheme for Underwater high pace verbal exchange Networks, commonly known as E2E-UCAN, is a critical technique for lowering plausible throughput by using efficiently warding off congestion inside a community. E2E-UCAN is a disbursed congestion control mechanism which uses statistics obtained from cease-to-end feedbacks to regulate sending prices on the give up systems. By means of monitoring the differences among expected network overall performance and actual performance, E2E-UCAN is able to pick out congestion stages inside the device, and is therefore able to regulate to prevent congestion before it happens. This enables to improve the stableness of the community and to increase the throughput of a network. Moreover, E2E-UCAN is likewise capable of efficaciously coping with queuing, avoiding packet loss in non-uniform networks, and in preferred, permitting applications to maximize performance.

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