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Large Model for Small Data: Foundation Model for Cross-Modal RF Human Activity Recognition 24

Large Model for Small Data: Foundation Model for Cross-Modal...

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22nd ACM Conference on Embedded Networked Sensor Systems, SenSys 2024

作者： Weng, Yuxuan Wu, Guoquan Zheng, Tianyue Yang, Yanbing Luo, Jun Department of Computer Science and Engineering Southern University of Science and Technology China Smart City Center Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology China College of Computer Science Sichuan University China College of Computing and Data Science Nanyang Technological University Singapore Singapore

ISBN: (纸本)9798400706974

Radio-Frequency (RF)-based Human Activity Recognition (HAR) rises as a promising solution for applications unamenable to techniques requiring computer visions. However, the scarcity of labeled RF data due to their non-interpretable nature poses a significant obstacle. Thanks to the recent breakthrough of foundation models (FMs), extracting deep semantic insights from unlabeled visual data become viable, yet these vision-based FMs fall short when applied to small RF datasets. To bridge this gap, we introduce FM-Fi, an innovative cross-modal framework engineered to translate the knowledge of vision-based FMs for enhancing RF-based HAR systems. FM-Fi involves a novel cross-modal contrastive knowledge distillation mechanism, enabling an RF encoder to inherit the interpretative power of FMs for achieving zero-shot learning. It also employs the intrinsic capabilities of FM and RF to remove extraneous features for better alignment between the two modalities. The framework is further refined through metric-based few-shot learning techniques, aiming to boost the performance for predefined HAR tasks. Comprehensive evaluations evidently indicate that FM-Fi rivals the effectiveness of vision-based methodologies, and the evaluation results provide empirical validation of FM-Fi's generalizability across various environments. © 2024 Copyright is held by the owner/author(s).

关键词： Zero-shot learning

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Security in Cloud Computing Using Blockchain: A Comprehensive Survey

Security in Cloud Computing Using Blockchain: A Comprehensiv...

引用

International Conference on Electrical and Electronics engineering, ICEEE 2023

作者： Jana, Sagnik Modak, Rahul Majumder, Koushik Dasgupta, Anurag Shaw, Rabindra Nath Ghosh, Ankush Department of Computer Science and Engineering Maulana Abul Kalam Azad University of Technology WB Kolkata India Computer Science and Engineering Technology Valdosta State University ValdostaGA31698 United States Chandigarh University Ajitgarh India

ISBN: (纸本)9789819986606

Cloud computing has revolutionized the business landscape by providing convenient access to data and applications via the Internet. However, its widespread adoption has also introduced significant security challenges. Blockchain, a decentralized and tamper-proof technology, offers promising solutions to tackle these security concerns in cloud computing. This paper aims to explore the potential of blockchain technology in bolstering security in cloud computing. It begins by examining the existing concept of security in cloud computing and introduces blockchain technology, highlighting its components, features, advantages, and limitations. Furthermore, it investigates the current blockchain-based solutions for securing cloud computing and conducts a comparative analysis to understand their benefits and limitations. By delving into these aspects, the paper seeks to provide a comprehensive understanding of how blockchain technology can enhance cloud computing security. Its primary goal is to inspire and encourage future research and development in this field, promoting the continued exploration of blockchain’s potential in ensuring the safety and integrity of cloud-based systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blockchain

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Revolutionizing Pest Detection for Sustainable Agriculture: A Transfer Learning Fusion Network with Attention-Triplet and Multi-Layer Ensemble 26

Revolutionizing Pest Detection for Sustainable Agriculture: ...

引用

26th International Conference on computer and Information technology, ICCIT 2023

作者： Haque, Nazmul Efat, Anwar Hossain Mahedy Hasan, S.M. Jannat, Nahrin Oishe, Mahjabin Mitu, Mostarina Rajshahi University of Engineering & Technology Department of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350359015

Pests, insidious and destructive, pose a significant threat to agriculture, stored goods, and the environment. Detecting and mitigating their impact is crucial for safeguarding crops, food safety, and economic stability. Automated detection systems have emerged as critical tools, offering rapid and precise identification for timely interventions. However, existing solutions grapple with limitations, such as extensive labeled datasets and struggles in adapting to new pest species. This study aims to overcome these challenges, developing a robust pest recognition system setting new standards in accuracy and adaptability. Meticulous dataset collection and preprocessing form the foundation for accurate pest recognition. Leveraging pre-trained models and fine-tuning contribute significantly. Integrating Transfer Learning Fusion (TLF) harmoniously blends deep features, and Multi-Layer Ensemble (MLE) techniques enhance performance, achieving 98% accuracy. Our approach resulted in a model with the highest accuracy and precision in pest recognition, surpassing previous limitations. Our work signifies progress in agriculture, emphasizing the importance of automated pest detection and providing a superior solution. Contributions mark a significant stride in safeguarding crops, preserving food safety, and bolstering agricultural sustainability. © 2023 IEEE.

关键词： Attention-Triplet (AT) Multi-Layer Ensemble (MLE) Pest Detection Transfer Learning Fusion (TLF)

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Attention-refined U-Net with Skip Connections for Effective Brain Tumor Segmentation from MRI Images 26

Attention-refined U-Net with Skip Connections for Effective ...

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26th International Conference on computer and Information technology, ICCIT 2023

作者： Rahman, A. F. M. Minhazur Hossain, Md. Ali Rajshahi University of Engineering & Technology Department of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350359015

Brain tumor segmentation in MRI scans is a crucial yet challenging task due to the high variability in tumor shape, size, and location. Accurate and efficient segmentation is of paramount importance for timely diagnosis and effective treatment planning in brain tumor patients. In response to these challenges, this paper presents an enhanced U-Net architecture named SC-SE U-Net. Our proposed model integrates Squeeze-and-Excitation channel attention blocks and skip connections similar to residual network architecture, into the traditional U-Net framework. The Squeeze-and-Excitation blocks enhance the model's capability to focus on more relevant feature maps and suppress less pertinent ones, thereby improving the overall segmentation accuracy. Simultaneously, the skip connections facilitate the flow of gradients during the training process, leading to more stable and faster convergence, and improved performance. We conducted an extensive evaluation of our proposed model on the TCGA-LGG brain tumor dataset. The results show that the SC-SE U-Net outperforms several existing segmentation methods, achieving an Intersection over Union score of 84.22% and a Dice score of 91.43%. The impressive performance of SC-SE U-Net underscores its potential to significantly improve diagnostic efficiency in clinical settings, highlighting the importance of further research in this direction. © 2023 IEEE.

关键词： Attention Mechanism Brain Tumor Low-Grade Glioma Magnetic Resonance Imaging Medical Image Segmentation Semantic Segmentation Skip Connection

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Towards the Deployment of Deep Learning Solutions in Plant Nutrient Deficiency Identification and Classification 5

Towards the Deployment of Deep Learning Solutions in Plant N...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Appalanaidu, Majji V. Kumaravelan, G. Hyderabad Institute of Technology and Management Department of Computer Science Engineering Hyderabad India Pondicherry University Department of Computer Science Karaikal India

ISBN: (纸本)9798350321425

In recent years, identifying plant nutrient deficiency is considered a critical issue in smart agriculture research. Identifying plant nutrient deficiency at an early stage will undoubtedly improve the quality and quantity of agricultural products. However, identifying the nutrient deficiency in the affected plant leaves is challenging for the farmers. Therefore, developing an automated system becomes essential to detect the deficiency so that remedies can be suggested according to the nutrient deficiency. In this context, Deep Learning (DL) models have recently been used to identify and classify the type of nutrient deficiency in plants. Hence, this work investigates the importance of deploying the various DL models such as MobileNet, MobileNetV2, DenseNet121, DenseNet169 and DenseNet201 in automatically classifying plant nutrient deficiency on the benchmark Rice, Wheat and Okra dataset. © 2023 IEEE.

关键词： Agricultural products

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Automatic Text Summarization based Web Application 14

Automatic Text Summarization based Web Application

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Gourh, Shubham Uniyal, Sushant Aryal, Ashish Beniwal, Tushar Samant, Surender Singh Graphic Era Hill University Department of Computer Science and Information Technology Dehradun India Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350335095

Text summarization is an important task in natural language processing that aims to condense large bodies of text into short but coherent summaries. This study provides an overview of text summarization techniques, including extractive, abstractive, and hybrid approaches. We explore various methods employed within each approach and discuss their advantages and disadvantages. Furthermore, we delve into the challenges faced by researchers in the field of text summarization and highlight potential avenues for future research. We introduce an all in one web application for text summarization that encompasses all aspects of text summarization. © 2023 IEEE.

关键词： Text processing

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Person Re-Identification: A Lightweight Feature Extraction Architecture for Image Based Methods 3

Person Re-Identification: A Lightweight Feature Extraction A...

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3rd International Conference on Electrical, computer and Communication engineering, ECCE 2023

作者： Hassan, Md Zahim Ahsan, Sk. Md. Masudul Khulna University of Engineering & Technology Department of Computer Science and Engineering Khulna9203 Bangladesh

ISBN: (纸本)9798350345360

Person re-identification network is widely used in person search method where these networks find correct person match from a gallery of images. Person re-identification has gained more attention among the researchers in recent years because of its use in security and other purposes. Existing methodologies in this area focuses on dealing with various challenges of person re-identification such as occlusion, lighting, camera viewpoint, clothing changes, etc. from different perspectives. In this article, we have used the Siamese model architecture in person re-identification. Different feature extractor models for the twin architecture have been implemented for learning to generate relevant feature vectors. An existing model has been used to improve its performance and proposed a new lightweight model architecture for feature extraction. Additionally, both model's performances have been analyzed by varying different hyper-parameters. Further, pre-trained models have also been used in the architecture and their performances comparison with other models is presented. In the whole process, A subset of publicly available MARS dataset is used while ensuring that it resembles all the qualities of the original dataset. Finally, it is presented that increasing parameters of the model can provide good results although almost similar outcome can be achieved with a decent smaller architecture too. © 2023 IEEE.

关键词： Feature extraction

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Qualitative Failure Analysis of IoT-enabled Industrial Fire Detection and Prevention System 26

Qualitative Failure Analysis of IoT-enabled Industrial Fire ...

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26th International Conference on computer and Information technology, ICCIT 2023

作者： Rahman, Md Mokhlesur Abdulhamid, Alhassan Kabir, Sohag Military Institute of Science and Technology Department of Computer Science and Engineering Bangladesh University of Bradford School of Computer Science AI and Electronics United Kingdom

ISBN: (纸本)9798350359015

The Internet of Things (IoT) has improved our lives through various applications such as home automation, smart city monitoring, environmental monitoring, intelligent farming, and a host of others. IoT is increasingly being used for environmental monitoring to prevent fire incidents and other environmental hazards. However, for IoT systems to function effectively in preventing fire incidents, they must operate in a safe, reliable, and dependable manner. The intelligent sensors and devices that constitute the system are prone to different types of failures, which can lead to unsafe or dangerous conditions. Failure of a fire prevention system can pose significant risks to Health, Safety, and the Environment (HSE). To address these concerns, it is essential to understand how component failures can contribute to the overall system failure. This paper adopts Fault Tree Analysis, a widely used framework for failure behaviour analysis in other safety-critical domains, to qualitatively analyse an intelligent fire detection system in an industrial setting. The analysis outlines the ways in which the system can fail and the necessary prevention mechanism to guard against undesired system failure. © 2023 IEEE.

关键词： fault tree analysis Internet of Things smart fire control system system failure analysis

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FL@FM-TheWebConf’24: International Workshop on Federated Foundation Models for the Web 24

FL@FM-TheWebConf’24: International Workshop on Federated Fo...

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33rd Companion of the ACM World Wide Web Conference, WWW 2023

作者： King, Irwin Long, Guodong Xu, Zenglin Yu, Han Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Hong Kong Faculty of Engineering and IT University of Technology Sydney Australia School of Computer Science and Technology Harbin Institute of Technology Shenzhen China College of Computing and Data Science Nanyang Technological University Singapore Singapore

ISBN: (纸本)9798400701726

In the evolving landscape of machine learning research, two significant developments have emerged to prominence: foundation models and federated learning. The FL@FM-TheWebConf’24 workshop provides an exciting forum for researchers and practitioners to discuss promising works and chart potential trajectories forward for this emerging field. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Frequency modulation

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Protein Structure Prediction in Structural Genomics without Alignment Using Support Vector Machine with Fuzzy Logic 3

Protein Structure Prediction in Structural Genomics without ...

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3rd International Conference on Electrical, computer and Communication engineering, ECCE 2023

作者： Saha, Sharnali Shill, Pintu Chandra Khulna University of Engineering & Technology Department of Computer Science and Engineering Khulna9203 Bangladesh

ISBN: (纸本)9798350345360

Protein secondary structure prediction from amino acid sequences is a challenging and complex task as it has become a must in oder to identifying the similarities/dissimilarities between protein structure. The protein secondary structure is used for studying the biological functionality of species in order to develop new drugs. A sustainable number of research has been done for predicting protein structure but yet the performance is not satisfactory. For this reason, it is necessary and time demanding to develop a technique for predicting protein structure that gives the satisfactory performance for large datasets termed as big datasets. In this article, propose a method based on the support vector machine and fuzzy logic in order to predict protein secondary structure without alignment. In this case, generate the optimal hyper plane of support vector machine using the membership values. Moreover, in order to increase the generalization ability a hybrid kernel support vector machine is propose that gives the better results in terms of classification and learning ability. We have tested the proposed method performance on the several benchmark datasets. The simulation results shows that the proposed technique outperforms better than other existing conventional techniques. © 2023 IEEE.

关键词： Fuzzy logic

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