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A Flow-Based Performance Evaluation on RYU SDN Controller

Journal of The Institution of Engineers (India): Series B 2024年第2期105卷 203-215页

作者： Ram, Anil Dutta, Manash Pratim Chakraborty, Swarnendu Kumar Department of Computer Science and Engineering NIT Jote Arunachal Pradesh Jote Itanagar791113 India Department of Computer Science and Information Technology Cotton University Pan Bazaar Assam Guwahati781001 India

Software-defined networking (SDN) is software-based networking technology which overcomes the complication tackled by traditional networking structures. The knowledge behind the SDN gives more flexibility to handle network as compared to traditional network since SDN detached the control plane (CP) from the data plane. As the SDN is a software-based technology, evaluation of performance is needed. The heart of SDN is SDN controller which is located in CP. SDN controller is accountable for the monitoring, decision making and investigation of factual time data traffic. Due to substantial rise of distributed real-time applications, there is a prerequisite for an efficient controller in networking sectors, data centers, academic and examination. As a result, it is critical to look at the presentation of a free-source specified controller in order to offer effective traffic routing, which will lead to better resource usage and improved network performance matrices. For network traffic analysis, the study proposes an execution of SDN structural design utilizing an RYU SDN controller. The present spadework assesses the performance of a bespoke network topology based on SDN architecture for device-to-device performance characteristics such as round trip time, bandwidth, and throughput. In the present default network topology for SDN, the simulation results show that our approach outperforms its competitors. © The Institution of Engineers (India) 2024.

关键词： Controllers

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A Multi-scale Feature Embedding Extension Network for RGB-D Cross-modal Person Re-identification 7

A Multi-scale Feature Embedding Extension Network for RGB-D ...

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7th World Conference on Computing and Communication Technologies, WCCCT 2024

作者： Dai, Xuesong Li, Xiaohong Li, Kunyuan Liu, Xueliang Wang, Jing Hefei University of Technology School of Computer Science and Information Engineering Hefei China

ISBN: (纸本)9798350372328

The RGB-D cross-modal person re-identification task is a branch of the cross-modal person re-identification task, aiming to match pedestrian images between RGB and depth modalities. Its main challenge lies in the significant modality difference between RGB and depth images. Due to the limited research attention on this task, there is a lack of large-scale, high-quality training data, making existing methods struggle to capture effective correlational information between RGB and depth modalities. This paper proposes a novel feature embedding expansion network for RGB-D cross-modal person re-identification. The network introduces perturbations to the embedding of intermediate-layer features to generate multiple diversified embedding features, effectively alleviating the problem of limited training data for RGB-D tasks. Simultaneously, through the incorporation of deformable convolution operations and depth-wise separable convolution operations during the embedding feature generation, the model is encouraged to learn richer scale and channel information from the limited training data. Meanwhile, by controlling the distance between expanded feature embeddings and original feature embeddings for different modalities, we mitigate the significant modality differences. Experimental results on the public benchmark datasets BIWI and RobotPKU demonstrate that our approach outperforms state-of-the-art methods. © 2024 IEEE.

关键词： Convolution

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Attribute-based Medical Data Storage and Sharing Scheme 9

Attribute-based Medical Data Storage and Sharing Scheme

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9th International Conference on Intelligent Informatics and BioMedical sciences, ICIIBMS 2024

作者： Xue, Qingshui Li, Yinhang Shi, Mengyao Shanghai Institute of Technology School of Computer Science and Information Engineering Shanghai China

ISBN: (纸本)9798350363043

The rational use of patient medical and health data information has facilitated the development of medical research institutions. An attribute-based medical data-sharing scheme is proposed in response to the current issues of vulnerability to leakage of patients' Electronic Medical Records (EMR) and the uncontrollable use of medical data by patients. Firstly, an attribute-based proxy re-encryption scheme with hidden access policies is introduced, to achieve dynamic data sharing without any disclosure of access policies. Secondly, to address issues such as single-point failures in centralized databases and excessive storage pressure on blockchains. Blockchain and IPFS are integrated to implement a model where key information is stored on the chain and complete data is stored off the chain, reducing the burden on the blockchain and ensuring the stability and security of the system. The results demonstrate that this scheme is more efficient than existing data-sharing solutions. © 2024 IEEE.

关键词： Electronic health record

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Targeted Influence Maximization Using Centrality and Diminishing Weight

Targeted Influence Maximization Using Centrality and Diminis...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Baruah, Khanjan Changmai Tripathi, Rohit Indian Institute of Information Technology Department of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798350350821

Social Networks are an integral part of our daily life. These networks are tools for sharing information among different users. In such a network, the most influential users become the source for disseminating information over the network. Hence, social network analysis was done to understand and identify the importance of a node in information diffusion. These influential nodes help to achieve maximum influence throughout the network. However, in certain cases, only a subset of nodes are needed to be influenced. This is called targeted influence maximization. In this paper, we use different centrality measures and diminishing weights to identify influential nodes and study the influence of these nodes on the target nodes. Our simulation shows that the seed selection scheme using centrality and diminishing weights activates more target nodes in a network than the existing schemes. © 2024 IEEE.

关键词： Centrality Measures Independent Cascade Model Social Network Analysis Target Influence Maximization

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ClipArtGAN: An Application of Pix2Pix Generative Adversarial Network for Clip Art Generation

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Multimedia Tools and Applications 2025年 1-25页

作者： Elnabawy, Reham H. Abdennadher, Slim Hellwich, Olaf Eldawlatly, Seif Technische Universität Berlin Berlin Germany Computer Science and Engineering Department Faculty of Media Engineering and Technology German University in Cairo Cairo Egypt Computer Science Department Faculty of Informatics and Computer Science German International University New Administrative Capital Egypt Computer and Systems Engineering Department Faculty of Engineering Ain Shams University 1 El-sarayat st. Abbassia Cairo Egypt Computer Science and Engineering Department The American University in Cairo Cairo Egypt School of Information Technology New Giza University Giza Egypt

An image can convey a thousand words. This statement emphasizes the importance of illustrating ideas visually rather than writing them down. Although detailed image representation is typically instructive, there are several contexts where simplification of image representations may be more informative. One way to provide an abstract representation of an object is via clip art, which is a simple graphic illustration of an object that can be included in documents or presentations. Clip art representation can be utilized in graphic design for concepts expression and daily life objects illustration. Furthermore, clip art had shown remarkable advantages when compared to real images in terms of editing and manipulation since clip art geometry can be easily edited in addition to the feasibility of storing it in a resolution-independent manner. However, it can be difficult to automatically create clip arts that resemble any object. Here, we propose a novel deep learning model, named ClipArtGAN, that utilizes Generative Adversarial Networks (GANs) to convert a photo of an object to its corresponding clip art. To our knowledge, this is the first GAN model based on Pix2Pix architecture that is capable of automatically generating clip art for any arbitrary object. Three training datasets were collected to train and compare two GAN models based on Pix2Pix and CycleGAN architectures. Our results demonstrate that the mean accuracy obtained using ClipArtGAN utilizing Pix2Pix outperforms that of CycleGAN by 58% and that of Google Images-based search by 54%. These results indicate the efficacy of ClipArtGAN in generating representative clip art images. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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Improving Depth Map Geometric Consistency Using an Enhanced Transformer Latent Code Model 9

Improving Depth Map Geometric Consistency Using an Enhanced ...

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9th International Conference on Intelligent Informatics and BioMedical sciences, ICIIBMS 2024

作者： Zhang, Chenyu Shanghai Institute of Technology School of Computer Science and Information Engineering Shanghai China

ISBN: (纸本)9798350363043

In structured light 3D measurement technology, the core challenge is recovering the phase and solving for depth from a single-frame fringe pattern. The introduction of deep learning techniques has significantly enhanced the efficiency of this process. However, the depth maps generated by existing deep learning methods often exhibit noise and inaccuracies. Accurate recovery of the depth map is crucial for 3D reconstruction. This paper proposes a Transformer-based latent code prediction network to optimize the geometric consistency of depth maps. A Transformer encoder maps the depth map into a low-dimensional latent code space, where feature extraction and fusion are conducted, and a decoder reconstructs high-quality depth maps. To improve the geometric accuracy of the depth maps, a geometry-aware loss function is introduced. Our experiments, conducted using the Adam optimizer within the PyTorch framework, demonstrate that our method achieves superior performance, producing depth maps with smoother surfaces and more accurate edges. © 2024 IEEE.

关键词： 3D reconstruction

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Next-Generation Consumer Electronics Data Auditing Scheme Toward Cloud-Edge Distributed and Resilient Machine Learning

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 2244-2256页

作者： Li, Yi Shen, Jian Vijayakumar, Pandi Lai, Chin-Feng Sivaraman, Audithan Sharma, Pradip Kumar Nanjing University of Information Science and Technology School of Computer Science Nanjing210044 China Zhejiang Sci-Tech University School of Information Science and Engineering Hangzhou310018 China University College of Engineering Tindivanam Anna University Chennai Department of Computer Science and Engineering Tindivanam604001 India National Cheng Kung University Department of Engineering Science Tainan701 Taiwan Netaji Subhas University of Technology Department of Information Technology New Delhi110078 India University of Aberdeen Department of Computing Science AberdeenAB24 3UE United Kingdom

Distributed and resilient machine learning (DRML) endues next-generation consumer electronics with AI function. Intuitively, AI provides innovative, humanized, convenient applications based on the data extended by next-generation consumer electronics. Cloud-edge computing is an ideal undertaken architecture of DRML due to its distributed property. However, as one of the core elements driving AI applications, data could be lost or corrupted owing to damaged electronics, unstable communication, and even cloud providers' malicious behavior. It is essential to ensure the data integrity of next-generation electronics before AI applications. To this end, we proposed a privacy-protection distributed data auditing scheme for cloud-edge DRML. An efficient data integrity verification method that only uses algebraic operation is constructed. Then, Function Secret Share (FSS) extends the data integrity verification method to protect consumer privacy. Besides, a consensus for data auditing results is designed among the edge servers. Finally, we present an abundance of theoretical analyses and experimental findings to substantiate and validate the efficiency and effectiveness of our proposed scheme. © 1975-2011 IEEE.

关键词： Edge computing

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An Efficient Image Contrast Enhancement using Segmentation in Genetic Algorithm for Remote Sensing Applications

An Efficient Image Contrast Enhancement using Segmentation i...

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2024 IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Yadav, Gyan Singh Nehra, Ajay Vashistha, Aman Gupta, Adarsh Deshmukh, Omkar Indian Institute of Information Technology Kota Department of Computer Science and Engineering India

ISBN: (纸本)9798350376876

This paper proposes a novel optimized histogram equalization technique using a genetic algorithm (GA) for brightness-preserved contrast enhancement. This method optimizes the histogram equalization technique to extract detailed data from distorted images. The principal objective is to enhance histogram equalization by fixing a trade-off between contrast improvement and mean-brightness preservation. A genetic algorithm is used to generate the new target histogram. The optimized histogram is used in the histogram equalization (HE) process to generate the final enhanced image. The performance of the proposed method is checked and validated with qualitative and quantitative assessments against well-known state-of-the-art techniques. The result section depicts that the proposed method significantly enhances the visual effect of distorted images based on human subjective and objective evaluation indices. 3-5 percent enhancement can be observed if we compare the standard Histogram Equalization method to the proposed method. © 2024 IEEE.

关键词： Genetic algorithms

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Design of a Fair Distributed Computing Platform Based on Distributed Ledger technology and Performance Measurements 1

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13th International Conference on Frontier Computing, FC 2023

作者： Liao, Bo-Yan Chang, Jia-Wei Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung Taiwan

ISBN: (数字)9789819993420

ISBN: (纸本)9789819993413

We propose a fair distributed computing platform based on Distributed Ledger technology (DLT) and performance measurements. The platform integrates DLT and federated learning, enabling users to train machine learning models on their local devices without compromising their privacy by sharing their data with a central server. Instead, only the trained model weights are uploaded to a central server for aggregation. To address privacy concerns associated with federated learning, we integrate various privacy-preserving methods, such as differential privacy, model pruning, and homomorphic encryption, into the platform framework. These techniques help protect user privacy while improving model accuracy. To address the non-IID data problem in federated learning, we use performance measurements to balance the training workload among users, and blacklist malicious users while incentivizing participation. DLT ensures the security and integrity of the platform by validating and recording all data transactions on the ledger. Overall, the proposed platform has the potential to revolutionize machine learning model training by making it more efficient, secure, fair, and transparent. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Blockchain

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Unsupervised Normative Learning for Quality Assessment on Diffusion MRI 22

Unsupervised Normative Learning for Quality Assessment on Di...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Yu, Jiahao Wang, Tenglong He, Yifei Pan, Yiang He, Jianzhong Wu, Ye School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China College of Information Engineering Zhejiang University of Technology Hangzhou China

ISBN: (纸本)9798331520526

Quality assessment of diffusion MRI (dMRI) aims to ensure that the presence of image artifacts does not affect the results of subsequent image analysis. Recently, a variety of deep learning methods have been proposed to enhance the quality and utility of these acquisitions. Still, they require a large amount of training data with artifact labeling. In this work, we developed an Unsupervised Quality Assessment tool (UNL-QA) for dMRI data that can handle a variety of artifacts such as ghosting, spike, noise and swap. Our method uses a vector quantized variational autoencoder to train typical patterns on a standard dMRI dataset, achieving 99.732% accuracy in detecting artifacts and standard data and 95% sensitivity in detecting artifacts on the training dataset. Performance across datasets is also excellent. It has also been demonstrated to be replicable on other datasets with different acquisition parameters. Without labeled artifact images necessary, UNL-QA allows fast and paves the way for efficient and effective artifact detection in large datasets. The code is available at https://***/yjh200319/***. © 2025 IEEE.

关键词： Damage detection

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