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the Application of Target Embedding AutoEncoder based on Knowledge Distillation in Multi-View Multi-Label Classification 4

The Application of Target Embedding AutoEncoder based on Kno...

引用

4th international conference on Computer engineering and Application, ICCEA 2023

作者： Zou, Xiaoyan Zhong, Ying Li, Jianmin Xie, Yanqi Yin, Huayi Ma, Ying School of Computer Science Xiamen University of Technology Xiamen China

ISBN: (纸本)9798350347548

In multi-view learning, how to effectively integrate features among multiple views becomes a key challenge. For multi-label tasks, it is extremely important that the relationship between features and labels. this paper proposes a Target Embedding Autoencoder framework based on knowledge distillation (tea-mvml), which explores the correlation between features and labels in Multi-View Multi-Label (MVML) learning. the framework transfers knowledge from a teacher model based on a Target Embedding Autoencoder (TEA) to a small student model through knowledge distillation. the teacher model of tea-mvml learns the relationship between features and labels in the potential space, while the student model has the generalization ability of the teacher model. Experimental results on multiple real-world datasets show that tea-mvml not only reduces the complexity of the model, but also outperforms other state-of-the-art multi-view multi-label classification approaches. © 2023 IEEE.

关键词： Distillation

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Efficient Vision Transformers for Edge Devices: Pruning and Quantization Approaches 4

Efficient Vision Transformers for Edge Devices: Pruning and ...

引用

4th international conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Pareek, Shashank Saleh Al-Samalek, Ahmed Alkhayyat, Ahmed Singh, Sandeep Singh, Amrita Dasi, Shivakrishna Chandigarh University Department of Computer Science and Engineering Mohali India Al-Zahrawi University College Department of Medical Laboratory Technics Karbala Iraq College of Technical Engineering The Islamic University Najaf Iraq School of Engineering and Technology Bahra University Department of Civil Engineering Waknaghat HP Solan India Chandigarh Engineering College Chandigarh Group of Colleges Department of Computer Science Engineering Jhanjeri Punjab Mohali40307 India Nims School of Computing Science and Artificial Intelligence Nims University Rajasthan Jaipur India

ISBN: (纸本)9798350387490

In this paper, background of pruning and quan-tization are discussed as well as carrying out the methods on Vision Transformers (ViT) with aim of improving efficiency and readiness for use on edge devices. the study considers the effects of these optimizations for model accuracy, size, time for inference, and energy expenditure. Reduction in size and computation demand is considerably achieved through pruning but this is at the expense of accuracy of the model. Quantization of models is beneficial as it reduces the size of the model and the time taken during inference, these are ideal for environments that have limited resources but it comes with drawbacks that are associated with quantization. Using both yields to the model as compact and efficient as possible but with a small trade off of accuracy. Some of the issues we consider are the question of how to op-timize trade-off between precision and speed, methods to reduce quantization effects, and techniques to make models conform to a given hardware system. the study determines that although existing techniques explain ways of advancing efficiency, they bring in new challenges and constraints. Further, there is a need to learn more about more sophisticated techniques concerning pruning and combinations of quantization approaches, as well as hardware wit tailored to perform low-precision calculations. Besides, the above approaches also need to be generalized into various application fields and enhance the model's practicality and stability. the study enriches the current knowledge base concerning the improvements of Vision Transformers for application on edge equipment while underlining the significance of the further work on model effectiveness and hardware implementation. this work is beneficial as it prepares the ground for innovations in applying state-of-art deep learning models in environments that are limited by resource availability in order to design better practical solutions in AI. © 2024 IEEE.

关键词： Distribution transformers

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Flexible Manufacturing Systems: CAD Programs to Simulate the Movements of Industrial Robots 4

Flexible Manufacturing Systems: CAD Programs to Simulate the...

引用

4th international conference on Electrical, Communication and Computer engineering, ICECCE 2023

作者： Morano-Okuno, Hector Rafael Caltenco-Castillo, Rafael Sandoval-Benitez, Guillermo Tecnologico de Monterrey School of Engineering and Science Mexico

ISBN: (纸本)9798350369694

Currently, the simulation and programming of industrial robots are carried out using commercial software applications from robot manufacturing companies. these software applications are expensive, and some require installation on robust computing equipment. In this work, the experiences obtained during the Manufacturing Systems Automation course for Mechatronic engineering students are shared. During this course, different configurations of industrial robots were used, and simulations of the trajectory of the end effector were carried out using a CAD software application aimed at the Academy with free licenses for students. the objective of this implementation was to 1) generate simulation solutions for different configurations of industrial robots through a CAD software application and 2) determine if there was a positive evolution or progress to create said simulation solutions, which were presented with an increasing level of difficulty. Both items were evaluated using a checklist. the results demonstrate that using CAD software applications is an option to generate simulation solutions for the trajectories of industrial robots in an accessible way and that they are digital teaching tools acceptable to students, promoting Human-Computer Interaction. © 2023 IEEE.

关键词： Students

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A Review on the Impact of Cognitive Factors in Introductory programming 4th

A Review on the Impact of Cognitive Factors in Introductory ...

引用

4th international conference on Communication, Computing and Electronics Systems, ICCCES 2022

作者： Kaur, Amanpreet Chahal, Kuljit Kaur Department of Computer Science Guru Nanak Dev University Amritsar143005 India

ISBN: (纸本)9789811977527

Understanding the cognitive factors that contribute to introductory programming students’ abilities to learn to program is critical to helping computer educators create better opportunities for students to improve their programming performance. the goal of this research is to explore cognitive factors that have an influence on programming performance in introductory programming courses in particular. the study documents 17 factors from 25 empirical studies that analyzed the influence of these factors on programming performance. Our analysis shows a wide range of cognitive factors studied and interrelated groups of factors studied in literature focused on introductory programming courses. this is a valuable review of information regarding influencing cognitive factors to restructure aspects of future introductory programming course curricula to benefit students’ ability to learn to program. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Students

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Hybrid Evaluation for Occlusion-based Explanations on CNN Inference Queries 40

Hybrid Evaluation for Occlusion-based Explanations on CNN In...

引用

40th IEEE international conference on Data engineering, ICDE 2024

作者： Ding, Guangyao Xu, Chen Qian, Weining School of Data Science and Engineering East China Normal University Shanghai200062 China Shanghai Engineering Research Center of Big Data Management China

ISBN: (纸本)9798350317152

Deep CNNs are increasingly prevalent in various application domains such as image processing. To explain a CNN prediction, it is popular to employ occlusion-based explanations (OBE). OBE helps users understand which parts of an image are important to a CNN prediction. Existing systems have explored incremental evaluation to accelerate CNN inference in OBE. However, they are oblivious that incremental evaluation does not always outperform full evaluation for certain layers. To address this issue, we propose a hybrid evaluation to efficiently interleave full and incremental evaluations during the CNN inference. Ad-ditionally, it employs a cost model to compare the overhead costs of two types of evaluations and a heuristic method to determine the efficient plan combination for common CNNs. More impor-tantly, hybrid evaluation adopts a dynamic programming-based method for attention-based CNNs. In particular, the dynamic programming-based method significantly reduces the overhead of searching for the efficient plan combination on the complex DAG structure. To demonstrate the efficiency of our techniques, we implement HyInJ, a hybrid CNN inf erence system based on PyTorch. Our experiments show that HyInf reduces execution time by up to 22% on GPU and 55% on CPU in comparison to the state-of-the-art incremental evaluation. © 2024 IEEE.

关键词： Dynamic programming

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A Comprehensive Analysis of Unified Approaches for Revealing Code Clone Detection 4

A Comprehensive Analysis of Unified Approaches for Revealing...

引用

4th international conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Bhaskar, P. Vijay Geetika School of Computer Science and Engineering Lovely Professional University Phagwara India

ISBN: (纸本)9798331529635

Code clone detection can be crucial in software maintenance, because of clone occurrences have potential of causing errors, raise costs, or results in unsynchronized modify. Analyzing the limitation of the existing clone detection method, we found that while the token and AST thin methods have been rather successful at detecting simpler clone types, they are significantly inadequate when used for identifying Type III and Type IV clones. the latter are especially significant in cross-language cases in which mere conjunctions of syntactic predicates are insufficient. this paper introduces an enhanced method for code clone detection through combining token-based, abstract syntax tree based, program dependency graph based, and machine learning based clone detection methods in a single framework. In this paper, we developed a scalable approach using adaptive ensemble weighting and fine-tuned LLMs that improved the precision over all types of clones together with low FPs in both languages. the selective execution model of the proposed approach also improves resource utilization making it possible to work on large codebase. Experimental outcomes prove that the proposed approach has higher accuracy, precision, and less computation time compared with the state-of-the-art techniques for clone detection, which make it a valuable contribution for clone detection study. © 2024 IEEE.

关键词： Semantics

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Comparison of Proposed Plant Leaf Diseases Detection Algorithm with Existing State-Of-the-art Techniques 4

Comparison of Proposed Plant Leaf Diseases Detection Algorit...

引用

4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Mitra, Dipra Gupta, Shikha Computer Science and Engineering Chandigarh University Punjab India

ISBN: (纸本)9798350306118

Despite a significant increase in population, agriculture still supplies food for everyone. It is crucial to anticipate plant diseases in the agricultural sector at an early stage since doing so will ensure that there is enough food for everyone. Unfortunately, infections can be foreseen at such an early stage in the crop cycle. the paper's objective is to increase farmers' comprehension of cutting-edge methods for decreasing plant leaf disease. Since there are few vegetables other than potatoes, tomatoes, and mangoes, a precise algorithm and machine learning approaches have been designed to identify leaf diseases. this study takes into account samples of mango, tomato, and potato leaves that have anomalies. Based on the suggested algorithm, a comparison study has been conducted with the help of a confusion matrix. © 2023 IEEE.

关键词： Fruits

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DyFormer: A CNN-Transformer Network Combining Local and Global Attention for Optical Remote Sensing Object Detection 4

DyFormer: A CNN-Transformer Network Combining Local and Glob...

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4th international conference on artificial Intelligence, Virtual Reality and Visualization, AIVRV 2024

作者： Huang, Youxiang Wang, Qin Jiao, Donglai Wang, Shufei Wang, Yu Lu, Zhiyi Ohtsuki, Tomoaki Gui, Guan College of Telecommunications and Information Engineering Njupt Nanjing China Ai Research Center Nanjing Great Information Technology Co. Ltd Nanjing China Keio University Department of Information and Computer Science Yokohama Japan

ISBN: (纸本)9798331528744

Remote sensing image (RSI) object detection is crucial for natural disaster management, urban planning, and resource exploration. However, due to significant differences between RSIs and natural images (NIs), existing object detectors designed for NIs are not directly applicable to RSIs. CNN-based models typically require specialized attention modules to relate small targets to global contexts, while Transformer-based models often add modules to enhance detail extraction, increasing computational costs for edge deployment. To address these challenges, we propose DyFormer, a hybrid CNN-Transformer model that combines local and global attention mechanisms for effective feature fusion. Additionally, a feature residual pyramid network (FRPN) is introduced to enhance multi-scale feature integration. We conduct experiments on the open source NWPU VHR-10 dataset to evaluate the proposed DyFormer, achieving a mAP@0.5 of 61.7%, outperforms existing sort-of-the-art methods, demonstrating its effectiveness for RSI object detection tasks. © 2024 IEEE.

关键词： Optical remote sensing

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Design and Development of Tomato Leaf Classification Framework Using CNN 4

Design and Development of Tomato Leaf Classification Framewo...

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4th international conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Lais Ezaz Ahmed, Sayed Abu Kumar, Abhishek Singh, Ranjit Chandigarh University Department of Computer Science and Engineering Punjab Gharuan India

ISBN: (纸本)9798350387490

Crop production loss in agriculture is twenty-two percent because of biotic and abiotic stress/disease and living and non-living organisms. Producers must be able to identify these requirements from the beginning. Computer vision can help discover early illness patterns and clusters. Recent Deep learning-based computer vision advancements can recognise single or multi-biotic stress in tomato plant leaves. this investigation will use the PlantVillage dataset to segment objects. the model was trained with tagged, enriched, and augmented data. the Hybrid-CNN model was used to segment sick tomato plants. In object detection, the proposed Hybrid CNN outperforms the U-Net architecture and Seg-Net when paired with instance segmentation. this study used semantic segmentation data to identify and classify single-and multiple-leaf illnesses. the Hybrid-CNN algorithm's predicted output corresponds to that of the adapted U-Net, M -SegN et, and adapted U -Net models based on state-of-art evaluation parameters with other evaluation parameters such as Intersection over Union (IoU) and Mean IoU. the proposed model outperformed competitors, processing 1004 pictures in under 30 nanoseconds. With an accuracy of 98.580/0, the suggested model outperforms modified segmentation models. © 2024 IEEE.

关键词： Plant diseases

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Enhancing Air Quality Prediction with Hybrid Deep Learning Techniques: A Review 4

Enhancing Air Quality Prediction with Hybrid Deep Learning T...

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4th international conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Divya, J. Jaison, B. Computer Science and Design R.M.K. Engineering College India

ISBN: (纸本)9798331515720

Nowadays, air pollution is among the significant and crucial areas of concern to policymakers and environmental and public health experts internationally. It has been considered that bad air can badly affect human health, ecosystems, and climate change. this air pollution, or poor air quality, has been proven to have negatively life-threatening and serious impacts. Accurate and timely adaption of air quality for various impacts is critical to avert the adverse impacts. the paper aims to provide an in-depth review of recent advances in air quality prediction using hybrid deep learning techniques. Mostly, the current methods of predicting the quality of air face challenges, like the inability to reproduce the complex and non-linear characteristics of air pollution. To tackle the existing challenges that bedevil traditional approaches, deep learning, being part of artificial intelligence with the capacity to bolster the efficiency and accuracy of the projections, has been adopted. Hybrid deep learning models, in association with physical or statistical models, offer promising approaches to enhance air quality prediction. this paper introduces the state-of-the-art review of the major concepts and methodologies applied to air quality prediction with hybrid deep learning techniques, showing benefits and limits of such models, the summary of recent studies, and discussions on future research trends in this fast-growing and interesting field. © 2024 IEEE.

关键词： Prediction models

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