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2024 International Conference on Smart Devices, ICSD 2024

作者： Dutta, Chiranjit Moudgil, Suveg Aluvala, Srinivas Saxena, Virat Raj Devi, K. Singh, Jagendra SRM Institute of Science and Technology Department of Computer Science and Engineering Ghaziabad India Galgotias University School of Computer Science Engineering Greater Noida India SR University School of Computer Science & Artificial Intelligence Warangal India Raj Kumar Goel Institute of Technology Department of Computer Science Engineering Ghaziabad India DAV Autonomous College Department of Commerce Titilagarh India Bennett University School of Computer Science Engineering & Technology Greater Noida India

ISBN: (纸本)9798350394412

This study applies machine learning to determine rice crop production using sensor information from temperature, humidity, and water levels. This project looks forward to providing insights to maximize agricultural practices and allow data-driven decision-making in rice cultivation. This is in line with how well these disparate machine learning models can predict rice crop production when measured against precision, accuracy, recall, and F1 score. To some extent, this is evident, as this study found that the machine learning models can predict yield quite satisfactorily, of which the model Random Forest performs very well in terms of precision and accuracy. Among them were the exactitude of sensor data and the algorithm selection, along with subsequent model assessment, refinement, and the ingression of great datasets inclusive of a broader set of environmental characteristics and crop yields - that were considered to be crucial in improving the precision of yield prediction algorithms. Additionally, the use of machine learning in farming has a number of benefits, such as enabling farmers to make decisions about crop management, irrigation, and optimization of yield. Combining machine learning methodologies with sensor technology can optimize resource allocation, save water, and improve overall agricultural productivity. Further, this study will facilitate the implementation of smart irrigation systems and set the stage for data-driven decision-making in agriculture for better farming practices and food production worldwide. © 2024 IEEE.

关键词： Smart agriculture

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KeyEE:Enhancing Low-Resource Generative Event Extraction with Auxiliary Keyword Sub-Prompt

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Big data Mining and Analytics 2024年第2期7卷 547-560页

作者： Junwen Duan Xincheng Liao Ying An Jianxin Wang Hunan Key Laboratory of Bioinformatics School of Computer Science and EngineeringCentral South UniversityChangsha 410083China Big Data Institute Central South UniversityChangsha 410083China

Event Extraction(EE)is a key task in information extraction,which requires high-quality annotated data that are often costly to *** classification-based methods suffer from low-resource scenarios due to the lack of label semantics and fine-grained *** recent approaches have endeavored to address EE through a more data-efficient generative process,they often overlook event keywords,which are vital for *** tackle these challenges,we introduce KeyEE,a multi-prompt learning strategy that improves low-resource event extraction by Event Keywords Extraction(EKE).We suggest employing an auxiliary EKE sub-prompt and concurrently training both EE and EKE with a shared pre-trained language *** the auxiliary sub-prompt,KeyEE learns event keywords knowledge implicitly,thereby reducing the dependence on annotated ***,we investigate and analyze various EKE sub-prompt strategies to encourage further research in this *** experiments on benchmark datasets ACE2005 and ERE show that KeyEE achieves significant improvement in low-resource settings and sets new state-of-the-art results.

关键词： natural language processing Event Extraction(EE) Multi-Prompt Learning(MPL) low-resource

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Adaptive IoT-Based Monitoring System in Edge Network for Aquaponics Ecosystem 3

Adaptive IoT-Based Monitoring System in Edge Network for Aqu...

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3rd International Conference on Digital Transformation and Applications, ICDXA 2024

作者： Prasetya, Nyoman Wira Anto, Yuli Azis, Baskoro Budi, Marcelino Hans Setia School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia School of Design Bina Nusantara University Interior Design Department Jakarta Indonesia

ISBN: (纸本)9798350373424

Aquaponics is a modern method of farming and cultivating fish. With the various advantages offered, this method has become popular with the public. Water quality is the key to success in using the aquaponics method. Farmers must monitor water quality regularly and continuously to ensure that water conditions still meet standards as a place for fish to live and contain sufficient nutrients for plant growth. To overcome this problem, an adaptive aquaponics system was created to retrieve and display the data automatically. This system will provide data every minute. The system will be IoT-based and adaptive. Adaptive here means the system can adapt to the state of the existing internet connection. So that the system can be more resilient in overcoming connection problems, and the collected data will not be lost when the system is disconnected from the internet. © 2024 IEEE.

关键词： Fish

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PipeMoE: Accelerating Mixture-of-Experts through Adaptive Pipelining 42

PipeMoE: Accelerating Mixture-of-Experts through Adaptive Pi...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Shi, Shaohuai Pan, Xinglin Chu, Xiaowen Li, Bo Harbin Institute of Technology School of Computer Science and Technology Shenzhen China Hong Kong Baptist University Department of Computer Science Hong Kong Data Science and Analytics Thrust Hong Kong The Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

ISBN: (纸本)9798350334142

Large models have attracted much attention in the AI area. The sparsely activated mixture-of-experts (MoE) technique pushes the model size to a trillion-level with a sub-linear increase of computations as an MoE layer can be equipped with many separate experts, but only one or two experts need to be trained for each input data. However, the feature of dynamically activating experts of MoE introduces extensive communications in distributed training. In this work, we propose PipeMoE to adaptively pipeline the communications and computations in MoE to maximally hide the communication time. Specifically, we first identify the root reason why a higher pipeline degree does not always achieve better performance in training MoE models. Then we formulate an optimization problem that aims to minimize the training iteration time. To solve this problem, we build performance models for computation and communication tasks in MoE and develop an optimal solution to determine the pipeline degree such that the iteration time is minimal. We conduct extensive experiments with 174 typical MoE layers and two real-world NLP models on a 64-GPU cluster. Experimental results show that our PipeMoE almost always chooses the best pipeline degree and outperforms state-of-the-art MoE training systems by 5%-77% in training time. © 2023 IEEE.

关键词： Pipelines

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A novel approach for image retrieval in remote sensing using vision-language-based image caption generation

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Multimedia Tools and Applications 2025年第6期84卷 2985-3014页

作者： Yadav, Prem Shanker Tyagi, Dinesh Kumar Vipparthi, Santosh Kumar Department of Computer Science and Engineering Malaviya National Institute of Technology Rajasthan Jaipur302017 India School of Artificial Intelligence and Data Engineering Indian Institute of Technology Ropar Punjab Rupnagar140001 India

Recent advancements in satellite technologies have resulted in the emergence of Remote Sensing (RS) images. Hence, the primary imperative research domain is designing a precise retrieval model for retrieving the most pertinent images based on the query. Present Remote Sensing Image Retrieval (RSIR) systems use visual descriptors to characterize the primitives (such as various land-cover types) that are visible in the images. However, the visual descriptors are inadequate for defining the complicated content of RS images. To solve this problem, a new model is devised for image retrieval based on image captions. The goal is to generate textual illustrations with captions to define relations amongst objects precisely. Here, image captioning is attained based on the vision-language pre-training model. The image captions are utilized for generating features like term frequency-inverse document frequency (TF-IDF), length of text, and Bag of Words. Meanwhile, query text is utilized wherein features like TF-IDF, text length, and Bag of Words are obtained. The similarity between query text features and the image captions features has been computed on the basis of a hybrid similarity measure wherein weights are tuned with the proposed Honey Badger Political Optimizer (HBPO) to retrieve the image. The proposed HBPO provided enhanced efficiency with elevated precision of 93.3%, recall of 93.7%, F1-score of 93.5%, and Recall-Oriented Understudy for Gisting Evaluation (ROUGE) of 0.441. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Visual languages

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An Empirical Investigation of Docker Sockets for Privilege Escalation and Defensive Strategies 5

An Empirical Investigation of Docker Sockets for Privilege E...

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5th International Conference on Innovative data Communication Technologies and Application, ICIDCA 2024

作者： Rajyashree, R. Mathi, Senthilkumar Saravanan, G. Sakthivel, M. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India Department of Artificial Intelligence and Data Science Erode Sengunthar Engineering College Erode India Department of Computer Science and Engineering Erode Sengunthar Engineering College Erode India

Cloud-based infrastructures often leverage virtualization, but its implementation can be expensive. Traditional coding methods can lead to issues when transitioning code from one computing environment to another. In response, the container paradigm emerged to offer cost-effective and agile delivery. Containers differ from full machine virtualization by compactly encapsulating the entire software and its dependencies. Leveraging containers, developers can create more secure and efficient applications. Docker, a prominent containerization platform, facilitates the execution of docker images. The Docker Hub serves as a popular repository for various images. Given the importance of application security, especially in the face of threats like malware, ransomware, and data breaches, ensuring robust security is imperative. The paper investigates vulnerabilities within Docker containers and proposes defensive strategies to mitigate potential breaches. In addition, it investigates attacks involving Docker sockets and suggests preventive measures for non-root users. © 2024 The Authors. Published by Elsevier B.V.

关键词： Containers

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Generative adversarial networks with stochastic gradient descent with momentum algorithm for video-based facial expression

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International Journal of Information Technology (Singapore) 2024年第6期16卷 3703-3722页

作者： Cherian, Aswathy K. Vaidhehi, M. Arshey, M. Briskilal, J. Simpson, Serin V. Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Chennai India Department of AI & amp School of Computer Science & amp

Video data is an asset that may be used in various settings, such as a live broadcast on a personal blog or a security camera at a manufacturing facility. Both of these examples are examples of how video data can be used. It is becoming increasingly common practice across a wide range of applications to use a machine learning appliance as a tool for processing video. Recent years have seen significant advancements made in the field of machine learning in computer vision. These advancements have been achieved. The presentation of humans is approached or even surpassed in areas such as item identification, object categorization, and image segmentation. Despite this, challenging difficulties exist, such as identifying human emotions. This study aims to recognize human emotions by analyzing still images and motion pictures taken from motion pictures using numerous machine learning procedures. To accomplish this, neural networks constructed based on Generative Adversarial Networks (GAN) were used to classify each face picture obtained from a frame into one of the seven categories of facial emotions we chose. To communicate feelings, videos are mined for informative aspects such as audio, single, and multiple video frames. During this process stage, separate instances of the OpenSMILE and Inception-ResNet-v2 models extract feature vectors from the audio and frames. After that, numerous classification models are trained using stochastic gradient descent with the impetus approach (SGDMA). The findings from each of the pictures are compiled into a table, and from that, it is determined which facial expression was seen on-screen the most often throughout the film. The classification of audio feature vectors is accomplished with the application of GAN-SGDMA. The Inception-ResNet-v2 algorithm is utilized to recognize feelings conveyed by still photographs. The findings of several experiments suggest that the presented distributed model GAN-SGDMA could significantly boost the sp

关键词： Attention model Face recognition Generative Adversarial Networks (GAN) Stochastic gradient descent with momentum algorithm (SGDMA) Video surveillance

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Automated Adversarial-Attack Removal with SafetyNet Using ADGIT

Automated Adversarial-Attack Removal with SafetyNet Using AD...

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International Conference on Smart Computing & Communications (ICSCC)

作者： Steven Halim Arvio Anandi Vyos Devin Alexander Agung Santoso Gunawan Karli Eka Setiawan Data Science Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350363104

ISBN: (纸本)9798350363111

In the realm of artificial intelligence (AI), a notable challenge has surfaced: adversarial attacks, these attacks involve altering input data to mislead AI models. Developing defensive measures against adversarial attacks is necessary for a more reliable AI system to protect its users from potential harm. In response to the risk of adversarial attacks, this study aims to mitigate the risk of such attacks, especially in image classification tasks, by proposing Adversarial Detection Guided Input Transformation (ADGIT) as an architecture designed to handle such attacks. In this study, the author will experiment with creating such architecture and measure the quality of the proposed technique. ADGIT works by utilizing SafetyNet as an adversarial detector to detect and cleanse adversarial attacks. The author concludes that the proposed technique could improve robustness against adversarial attacks increasing consistent prediction accuracy from 43% to 60% and reconstructing adversarial input images to be more similar to their unperturbed version, with reconstructed images' PSNR score on 0.007 perturbation increased from 43.1182 to 89.0999. The technique proposed could be used as a new defensive measure and improving robustness against adversarial attacks. Although ADGIT is capable of handling adversarial samples, ADGIT has a drawback in performance speed due to the extra preprocessing step.

关键词： Accuracy Perturbation methods Detectors computer architecture Robustness data models Security Artificial intelligence Image reconstruction Image classification

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Evaluating Vision Transformers Efficiency in Image Captioning 4

Evaluating Vision Transformers Efficiency in Image Captionin...

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4th International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2024

作者： Samosir, Bintang Kevin Hizkia Isa, Sani Muhamad Binus University Binus Graduate Program Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798331506490

This study investigates the performance of Vision Transformer (ViT) variants - the Shifted Window Transformers (SWIN), Distillation with No Labels (DINO), and data-efficient Image Transformers (DeIT) - in image captioning tasks using the Flickr8K dataset. While ViT architectures have shown promise in image classification, their effectiveness for image captioning, particularly with smaller datasets, remains unclear. The models' performance was evaluated using BLEU metrics, while training efficiency was analyzed through Pareto front analysis of computational time and accuracy. Among the tested variants, SWIN Transformers demonstrated superior performance (BLEU-1: 64.4, BLEU-2: 33.9, BLEU-3: 17.1, BLEU-4: 8.4), followed by DINO (BLEU-1: 63.1, BLEU-2: 32.7, BLEU-3: 16.4, BLEU-4: 7.5), while DeIT showed the weakest performance (BLEU-1: 61.6, BLEU-2: 31.1, BLEU-3: 14.7, BLEU-4: 6.5). SWIN Transformers achieved the shortest training time at 3 minutes 31 seconds per epoch, making it the most efficient model among ViT variants based on Pareto front analysis. While ViT variants achieved competitive BLEU-1 scores comparable to previous top models, they struggled with generating coherent, longer sentences, as evidenced by suboptimal BLEU-4 scores. These findings provide empirical evidence of how the lack of inductive bias in transformer architectures affects their ability to capture complex scene relationships, despite their strong feature detection capabilities, contributing to the understanding of transformer models' limitations in vision-language tasks, especially with limited data. © 2024 IEEE.

关键词： Distribution transformers

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Cosmetics Recommendation System Based on Ingredients Using Cosine Similarity

Cosmetics Recommendation System Based on Ingredients Using C...

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data science and Its Applications (ICoDSA), International Conference on

作者： Sekar Alisha Firdaus Laksmi Dwi Nurmuthia Raina Imtiyaz Alexander Agung Santoso Gunawan Karli Eka Setiawan Data Science Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350365351

ISBN: (纸本)9798350365368

In the rapidly evolving beauty industry, consumers are often bombarded with an overwhelming array of skincare brands and products, making the quest for the perfect skincare regimen a daunting task. This saturation of the market not only confuses consumers but also poses the risk of resource wastage and potential skin damage due to incompatible ingredient combinations. To mitigate these challenges, our research presents an innovative recommendation system designed to streamline the product selection process. Utilizing the principle of cosine similarity, our methodology involves a detailed analysis of the ingredients contained in various skincare products. A quantitative foundation for evaluating ingredient lists of various skincare products is provided by cosine similarity, a mathematical metric that evaluates the similarity between two non-zero vectors by computing the cosine of the angle between them. Our algorithm generates customized product recommendations by thoroughly comprehending the intricate interactions among different constituents. This bespoke approach simplifies the decision-making process for consumers, enabling them to make well-informed choices that cater to their unique skin health needs. The effectiveness of our recommendation system is validated through comprehensive user feedback, demonstrating its potential to redefine the paradigm of personalized skincare recommendations within the beauty industry. Through providing customers with critical information and encouraging a culture of knowledgeable choice, we see a time when customized skincare products will not only increase customer satisfaction but also brand loyalty, which will be a big step toward the democratization of customized skincare.

关键词： Industries Measurement Deep learning Diversity methods Skin Vectors Software

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