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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： N. Gayathri H. Anandakumar S. Gowri J. Keerthika S. Nithyapriya Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore Tamilnadu Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore Tamilnadu Department of Artificial Intelligence and Data Science Bannai Amman Institute of Technology Coimbatore Tamilnadu

The primary motivator after the evolution of cognitive radio is the lack of utilization of the spectrum. This technology adds new functionality in the physical layer, data link sub-layer, Transmission Control Protocol/Internet Protocol layer, and Transport layers of the hierarchy of software layers. The majority of the currently available research in cognitive radio has concentrated on security challenges at the physical, MAC layers. Over the past two decades, cyber security threats and problems have grown significantly, especially in cognitive radio networks. This study describes and analyzes various attacks that target each layer at its protocol stack. Each attack's specific detection techniques and defenses are also examined and contrasted.

关键词： Protocols Computational modeling TCPIP Network security Physical layer Software Cognitive radio

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Low-rank matrix recovery with total generalized variation for defending adversarial examples

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Frontiers of Information Technology & Electronic engineering 2024年第3期25卷 432-445页

作者： Wen LI Hengyou WANG Lianzhi HUO Qiang HE Linlin CHEN Zhiquan HE Wing W.Y.Ng School of Science Beijing University of Civil Engineering and ArchitectureBeijing 100044China School of Computer Science and Engineering South China University of TechnologyGuangzhou 510006China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen 518060China Institute of Big Data Modeling and Technology Beijing University of Civil Engineering and ArchitectureBeijing 100044China

Low-rank matrix decomposition with first-order total variation(TV)regularization exhibits excellent performance in exploration of image *** advantage of its excellent performance in image denoising,we apply it to improve the robustness of deep neural ***,although TV regularization can improve the robustness of the model,it reduces the accuracy of normal samples due to its *** our work,we develop a new low-rank matrix recovery model,called LRTGV,which incorporates total generalized variation(TGV)regularization into the reweighted low-rank matrix recovery *** the proposed model,TGV is used to better reconstruct texture information without *** reweighted nuclear norm and Li-norm can enhance the global structure ***,the proposed LRTGV can destroy the structure of adversarial noise while re-enhancing the global structure and local texture of the *** solve the challenging optimal model issue,we propose an algorithm based on the alternating direction method of *** results show that the proposed algorithm has a certain defense capability against black-box attacks,and outperforms state-of-the-art low-rank matrix recovery methods in image restoration.

关键词： Total generalized variation Low-rank matrix Alternating direction method of multipliers Adversarial example

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A Restricted Parametrized Model for Interval-Valued Regression

A Restricted Parametrized Model for Interval-Valued Regressi...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Jingda Ying Shaily Kabir Christian Wagner Lab for Uncertainty in Data and Decision Making (LUCID) School of Computer Science University of Nottingham Nottingham UK Department of Computer Science and Engineering University of Dhaka Bangladesh

This paper explores the parameter generation of the existing ‘Parametrized Model’ (PM) as the state-of-the-art linear interval-valued regression model, highlighting that its strong performance may arise from unexpected behavior. Focusing on the approach's core idea of using dynamic reference points from the regressor variables to describe and regress interval-valued data, this paper shows that key parameters in the PM method are not actually restricted, and explores the impact this has on model generation. As part of this analysis, we propose a Restricted Parametrized Model (RPM) which ensures parameter bounds are maintained. We evaluate the approach by conducting linear regression for a series of synthetic interval-valued data sets with different features, discussing its performance against the original PM and other linear interval regression models. The experiments confirm empirically that the PM model violates restrictions on parameter bounds, resulting in the selection of reference points—outside—the regressor intervals–nevertheless producing strongly performing regression models which outperform other models, including RPM, when parameter bounds are violated. We conclude by discussing that the mechanism underpinning the PM regression is different to what is expected, i.e. it commonly selects reference points which are not from the regressor variables, warranting further research to explore whether the PM's parameter bounds can be relaxed, or whether this exposes potential problems, for example for edge cases.

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Research on Deep Clustering Based on Image data 13th

Research on Deep Clustering Based on Image Data

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13th International Conference on computer engineering and Networks, CENet 2023

作者： Li, Xuanyu Yang, Houqun Zhang, Xiaoying Yang, Dangui Huang, Jianqiang Gan, Lin College of Computer Science and Technology Hainan University Hainan Haikou570228 China Haikou Key Laboratory of Deep Learning and Big Data Application Technology Hainan University Hainan Haikou570228 China Hainan Haikou China Elmore Family School of Computer and Electrical Engineering Purdue University West Lafayette United States

ISBN: (纸本)9789819992386

Clustering is an important branch of unsupervised tasks, aiming at mining deeper relationships and patterns in data. The quality of feature representation based on image datasets often determines the upper limit of clustering tasks. In recent years, the application of deep learning in deep clustering representation learning module has learned better feature representation, which gradually overcomes the limitation of traditional shallow clustering facing high-dimensional unstructured data. Deep clustering has become a popular research direction in the unsupervised field in recent years, and high clustering performance has been obtained in the continuous deepening research. The existing deep clustering research is mainly oriented to various fields of artificial intelligence, including natural language processing (NLP), speech recognition (ASR), computer vision (CV), etc. We take the field of computer vision as the entry point, analyze and study the research progress of deep clustering in computer vision (CV), and deconstruct the deep clustering model into two parts: a detailed classification of the feature extraction module from the perspective of network model architecture, while the data space of clustering is used as the entry point to divide the clustering module, and discuss the deep clustering. Finally, we analyze the datasets and evaluation metrics commonly used in experiments to study deep clustering architectures in computer vision. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Feature extraction

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Early Childhood Action Monitoring and Analytics System (ECAMS) 28

Early Childhood Action Monitoring and Analytics System (ECAM...

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28th IEEE Pacific Rim International Symposium on Dependable Computing, PRDC 2023

作者： Andarage, Isuru Supasan Naotunna Fernando, Dileepa Lokuarachchi, Buddhi Avishka Athuluwage, Malithi Gimhani Wijewickrama, Pavithra Sri Lanka Technological Campus Department of Data Science Padukka10500 Sri Lanka Nanyang Technological University School of Computer Science and Engineering Singapore Singapore Sri Lanka Technological Campus Faculty of Engineering Padukka10500 Sri Lanka Sabaragamuwa University of Sri Lanka Department of Sport Sciences and Physical Education Belihuloya Sri Lanka

ISBN: (纸本)9798350358766

The cultivation of fundamental movement skills (FMS) during the early stages of childhood bears immense importance in shaping an individual's involvement and achievements in sports throughout their entire life. Therefore, the assessment of motor skills in early childhood (age 3-5 years) significantly influences the advancement of sports within a nation. To this end, early childhood motor skill assessment has been performed manually posing efficiency challenges. The current automation approaches either involve invasiveness or depend on a substantial volume of annotated video training data. In this research, we introduce the Early Childhood Actions Monitoring and Analytics System (ECAMS) toolkit designed to evaluate early childhood motor skills, including activities like sitting up, running, walking, and jumping. This research employs computer vision and machine learning techniques integrated with the TGM2D (Test of Gross Motor Development-2nd Edition) toolkit to accurately detect motor skills from a given video input. This real-time analysis will offer accurate guidance to individuals involved in early childhood development, including educators, trainers, parents, and sports analysts. © 2023 IEEE.

关键词： Sports

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Detection Method of River Floating Objects based on Edge Computing 2

Detection Method of River Floating Objects based on Edge Com...

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2nd International Conference on Robotics, Automation and Intelligent Control, ICRAIC 2022

作者： Li, He Jia, Huimei Zhang, Rui Yang, Shuaipeng Qi, Qinglei Liu, Tianyang Henan Costar Group Co. Ltd Nanyang China College of Information Engineering Henan University of Science and Technology Luoyang China Henan Engineering Research Center of Intelligent Processing for Big Data of Digital Image School of Computer Science and Technology Nanyang Normal University Nanyang China Sydney Business School Shanghai University Shanghai China

For the detection of objects floating in the river, most of the traditional intelligent video monitoring methods are used to monitor through manual viewing. During the traditional video data transmission and processing, the massive amount of data will put pressure on the background such as network and computing power. The new effective method is to crop and optimize YOLOV5s, add a specific image pre-processing module, and deploy it by edge computing, embed a SOC (System on Chip) chip in the web camera for real-time processing of video data. Simulation experiments prove that the above method can improve the accuracy and speed of the target detection network. © Published under licence by IOP Publishing Ltd.

关键词： Edge computing

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Few-Shot Learning-Based Machine Fault Diagnosis Using EMD-Gammatone Spectrogram with Limited Labeled Audio dataset

Few-Shot Learning-Based Machine Fault Diagnosis Using EMD-Ga...

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International Conference on Big data and Smart Computing (BIGCOMP)

作者： Mahe Zabin Syed Tasnimul Karim Ayon Farhan Md Siraj Mehedi Hasan Shuvo Ho-Jin Choi Jia Uddin School of Computing Korea Advanced Institute of Science and Technology Daejeon Republic of Korea Department of Computer Science and Engineering BRAC University Dhaka Bangladesh Department of Computer Science and Engineering Dhaka University of Engineering & Technology Gazipur Dhaka Bangladesh AI and Big Data Department Endicott College Woosong University Daejeon Republic of Korea

ISBN: (数字)9798331529024

ISBN: (纸本)9798331529031

The deep learning models are computationally burdensome, and their accuracy depends on the number of labeled datasets used in training. The scarcity of the labeled dataset in industrial machine diagnosis is a significant concern as it is pretty impossible to collect data from faulty machines in industrial environments. To address this issue, in this paper, we presented a few-shot learning-based machine fault diagnosis model on a lightweight SqueezeNet architecture using generated texture images from audio sensor data. Subsets of two public audio datasets, MIMII and ToyADMOS, containing limited data- 10 images per class were used to validate the model. The experimental results demonstrate that the model has exhibited 89.6% and 98.3% accuracy for MIMII and ToyADMOS, respectively, with only ten labeled images of each fault class. As the model has only 73 thousand trainable parameters and requires only 300 KB of memory, it is expected to be deployable in a portal device for machine fault diagnosis.

关键词： Fault diagnosis Training Accuracy Sensitivity Computational modeling Memory management data models Few shot learning Spectrogram Portals

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Document Ranking based on Similarity using Natural Language Processing Technique

Document Ranking based on Similarity using Natural Language ...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： M. Jasmine Ananthi Baby Shamini P Kalaivani S S. Santhosh Jency A P Rishik Electronics and Communication Engineering DMI College of Engineering Chennai Computer Science and Engineering RMK Engineering College Kavaraipettai India Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai India Electronics and Communication Engineering St.Joseph College of Engineering Artificial Intelligence and Data Science R.M.K College of Engineering and Technology Computer Science and Engineering RMK Engineering College Kavaraipettai TN India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

As a result of the growth of the internet, Information was digitalized as files and documents. Information retrieval is the process of obtaining the required documents from vast databases. As the number of documents multiplies every five years, It is crucial to retrieve information effectively. Therefore, automatic indexing of documents is necessary for efficient information retrieval. In this research, an unsupervised model using genism was introduced to solve the problem. However, in the related work, the challenges of Information retrieval such as classification of similar documents, extraction of keywords, scientific documents clustering and evaluating the similarity are addressed using Natural Language Methods. The research provides an unsupervised model using genism to rank the documents based on the user query. The proposed model preprocesses the documents, generates the similarity score according to the user's query and ranks the documents based on the similarity score. The proposed methodology makes it easier to quickly retrieve the relevant documents based on the user's needs. To evaluate the performance of the proposed method, we collected the documents from different domains on the internet. The proposed method obtains comparatively more relevant documents than the traditional methods.

关键词： Accuracy Computational modeling Entertainment industry Manuals Search engines Information retrieval Tokenization Engines Machine assisted indexing Digital information

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Fed-GAN: Federated Generative Adversarial Network with Privacy-Preserving for Cross-Device Scenarios

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Han, Song Ding, Hongxin Zhao, Shuai Ren, Siqi Zeng, Shengke Xue, Mengqi Wang, Ruili Hangzhou City University School of Computer and Computing Science Hangzhou China Zhejiang Gongshang University School of Computer Science and Technology China Zhejiang Key Laboratory of Big Data and Future E-Commerce Technology Hangzhou China Xihua University School of Computer and Software Engineering Chengdu China Massey University School of Mathematical and Computational Science Auckland New Zealand

Huge amounts of data from various sources are substantial to dependable distributed machine learning, especially for trustworthy federated learning (FL). However, existing FL methods are difficult to collect enough data for training the global model more accurately, especially in cross-device scenarios. In this paper, we propose a new federated generative adversarial network empowered by differential privacy and knowledge transfer named Fed-GAN, which can be used to address the problem of data shortage and prevent generator leakage from resource-constrained devices, as well as generate high-quality synthetic data while ensuring strict DP guarantees. Different from other generative model methods, our Fed-GAN framework can achieve efficient and secure generative model training and limited permission for resource-constrained devices to prevent them from leaking or misusing the generator. In addition, we propose a pHash-KT method for our Fed-GAN framework, which selects potentially high-quality data through the knowledge of each client for improving the utility of synthetic data. Our FedGAN framework satisfies (Formula presented)-DP, and also has high resistance when number of adversaries is 10%-70% of the total number of clients. Extensive experiments demonstrate that our Fed-GAN framework not only generates high-quality synthetic data, but also provides strict DP guarantees, compared with other generative model methods. © 2004-2012 IEEE.

关键词： Generative adversarial networks

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Question-Answer Cross Language Image Matching for Weakly Supervised Semantic Segmentation

arXiv

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arXiv 2024年

作者： Deng, Songhe Zhuo, Wei Xie, Jinheng Shen, Linlin Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China

Class Activation Map (CAM) has emerged as a popular tool for weakly supervised semantic segmentation (WSSS), allowing the localization of object regions in an image using only image-level labels. However, existing CAM methods suffer from under-activation of target object regions and false-activation of background regions due to the fact that a lack of detailed supervision can hinder the model’s ability to understand the image as a whole. In this paper, we propose a novel Question-Answer Cross-Language-Image Matching framework for WSSS (QA-CLIMS), leveraging the vision-language foundation model to maximize the text-based understanding of images and guide the generation of activation maps. First, a series of carefully designed questions are posed to the VQA (Visual Question Answering) model with Question-Answer Prompt engineering (QAPE) to generate a corpus of both foreground target objects and backgrounds that are adaptive to query images. We then employ contrastive learning in a Region Image Text Contrastive (RITC) network to compare the obtained foreground and background regions with the generated corpus. Our approach exploits the rich textual information from the open vocabulary as additional supervision, enabling the model to generate high-quality CAMs with a more complete object region and reduce false-activation of background regions. We conduct extensive analysis to validate the proposed method and show that our approach performs state-of-the-art on both PASCAL VOC 2012 and MS COCO datasets. Code is available at: https://***/CVI-SZU/QA-CLIMS Copyright © 2024, The Authors. All rights reserved.

关键词： Chemical activation

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