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Wholeness Unveiled: Pioneering the Fusion of Denoising and Inpainting for Mastery in Image Restoration 4

Wholeness Unveiled: Pioneering the Fusion of Denoising and I...

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4th international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Ashwini, R. Vani, R. Suvitha, S. Faculty of Engineering and Technology Srm Institute of Science and Engg. Tamil Nadu Ramapuram India Faculty of Engineering and Technology Srm Institute of Science and Engg. Ramapuram Tamil Nadu Chennai600089 India S.A.Engineering College Department of Ece Avadi Chennai600077 India

ISBN: (纸本)9798350343670

Effective image denoising techniques and inpainting are frequently needed for mural art restoration and preservation in order to accomplish accurate reconstruction. this comparative study examines the effectiveness of three different methods for denoising mural art images: generative adversarial network (GAN) based denoising, deep convolutional neural network (CNN) based denoising, and conventional methods like Discrete Wavelet Transform (DWT) and Stationary Wavelet Transform (SWT) techniques. Due to their extraordinary capacity to extract complex patterns and features from noisy data, deep learning approaches have recently become more popular in the field of picture denoising. In order to denoise photos of mural art, this study investigates the use of CNNs and GANs, which include a generator network and a generator network, respectively. these models will be trained and tested on a variety of noisy mural art datasets, and their denoising performance will be compared in terms of both quantitative measures and visual quality. Additionally, the study uses DWT and SWT, two conventional denoising techniques, as benchmarks. these techniques provide an alternative viewpoint and are commonly utilised in image processing. the comparison analysis takes into account elements including the effectiveness of the denoising process, the preservation of tiny details, and overall image quality. this study makes a contribution by thoroughly comparing these denoising methods in the context of restoring mural painting. Combining denoising and image inpainting techniques can be a powerful approach for restoring and enhancing images. Practitioners and researchers can choose the best denoising strategies for particular settings by carefully weighing the advantages and drawbacks of each approach. the findings of this study aim to guide the selection of appropriate methods based on the characteristics of the mural art images and the desired quality of the reconstructed results. © 2024 IEEE.

关键词： Deep Convolutional Neural Network Generative Adversarial Network (GAN) Image denoising Image Inpainting

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Cross-Language Interoperability of Heterogeneous Code 23

Cross-Language Interoperability of Heterogeneous Code

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7th international conference on the art, science, and engineering of programming (programming)

作者： Stratikopoulos, Athanasios Blanaru, Florin Fumero, Juan Xekalaki, Maria Papadakis, Orion Kotselidis, Christos Univ Manchester Manchester Lancs England OctoML Seattle WA USA

ISBN: (纸本)9798400707551

In recent years, the Java Virtual Machine has evolved from a cross-ISA virtualization layer to a system that can also offer multilingual support. GraalVM paved the way to enable the interoperability of Java with other programming languages, such as Java, Python, R and even C++, that can run on top of the Truffle framework in a unified manner. Additionally, there have been numerous academic and industrial endeavors to bridge the gap between the JVM and modern heterogeneous hardware resources. All these efforts beacon the opportunity to use the JVM as a unified system that enables interoperability between multiple programming languages and multiple heterogeneous hardware resources. In this paper, we focus on the interoperability of code that accelerates applications on heterogeneous hardware with multiple programming languages. To realize that concept, we employ TornadoVM, a state-of-the-art software for enabling various JDK distributions to exploit hardware acceleration. Although TornadoVM can transparently generate heterogeneous code at runtime, there are several challenges that hinder the portability of the generated code to other programming languages and systems. therefore, we analyze all challenges and propose a set of modifications at the compiler and runtime levels to constitute Java as a prototyping language for the generation of heterogeneous code that can be used by other programming languages and systems.

关键词： code interoperability programming languages heterogeneous hardware

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Hotel Service Robot Control System 4

Hotel Service Robot Control System

引用

4th international conference on artificial Intelligence, Robotics, and Communication, ICAIRC 2024

作者： Yu, Hao Gong, Fan Zheng, Qi Guangzhou Institute of Science and Technology Faculty of Intelligent Manufacturing and Electrical Engineering Guangzhou China

ISBN: (纸本)9798331531225

With the advancement of technology and the growing demand for intelligent and efficient services, the traditional hotel service model is increasingly unable to meet people's expectations for personalized and non-contact experiences. In this context, this study proposes an STM32-based control system for hotel service robots. the system integrates motor control, voice recognition, touch screen interaction and an innovative doorbell module to improve service quality. It also runs the bidirectional A∗ path planning algorithm with the help of Raspberry Pi, which enables the robot to navigate autonomously in complex hotel environments and perform precise food delivery and guest guidance tasks. Experimental results show that this system can improve the service efficiency of hotels and reduce the waste of laboratory resources in the traditional hotel industry, thus promoting the transformation of traditional hotels into smart hotels, while improving customer experience, attracting more guests and enhancing the competitiveness of hotels. © 2024 IEEE.

关键词： Robot programming

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From Inception to Efficiency: Evaluating State-of-the-art CNNs for Fake Currency Identification 4

From Inception to Efficiency: Evaluating State-of-the-Art CN...

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4th IEEE international conference on Emerging Frontiers in Electrical and Electronic Technologies, ICEFEET 2024

作者： Aryan, Raj Kumar, Kakelli Anil Gupta, Anjaneya Parikh, Manav School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798331528614

this paper presents a comprehensive comparison of five state-of-the-art deep learning models for fake currency detection using the Indian Currency Dataset. the performance of Inception V3, ResNet50, Xception and EfficientNet B0 models are evaluated, with the experimental findings indicating that ResNet significantly surpasses the performance of other models, achieving an accuracy of 98.78%, while EfficientNet B0 got 97.82% and Xception got 97.09%. An in-depth analysis of each model's performance is provided, discussing their architectural differences and their impact on currency authentication tasks. this study advances the field of automated banknote verification and provides key insights for integrating advanced neural networks into financial security frameworks. ©2024 IEEE.

关键词： Convolutional neural networks

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Enhancing programming Course Quality through Human Posture Recognition and Screen Content Detection 4

Enhancing Programming Course Quality through Human Posture R...

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4th international conference on Information science and Education, ICISE-IE 2023

作者： Chen, Xiaopan Bai, Haiyang Zhu, Xiaoke Dong, Lingyuan School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology Henan University Kaifeng China Henan University Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China

ISBN: (纸本)9798350394610

the evaluation of programming course instruction is a key component of educational management. However, relying solely on supervising teachers for assessment poses challenges in comprehensive evaluation and providing feedback on students' classroom learning status. Additionally, a significant amount of monitoring video data from programming courses in Chinese universities remains underutilized. To address these challenges, this paper combines traditional educational management with artificial intelligence and proposes an intelligent algorithm for detecting students' learning status in programming courses. We first establish a dual YOLO network model to analyze students' postures and screen content in programming classroom monitoring videos, thereby identifying their behaviors. Subsequently, we develop a programming Classroom Student Behavior Recognition System (PCSBS) that integrates data collection, student behavior detection, and result visualization functionalities. Experimental results demonstrate that the system can accurately provide feedback on students' programming course learning status, offering graded results that assist supervising teachers in their classroom instructional evaluation tasks to enhance educational management efficiency. © 2023 IEEE.

关键词： Students

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FASTFIXER: An Efficient and Effective Approach for Repairing programming Assignments 24

FASTFIXER: An Efficient and Effective Approach for Repairing...

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39th ACM/IEEE international conference on Automated Software engineering (ASE)

作者： Liu, Fang Liu, Zhenwei Zhao, Qianhui Jiang, Jing Zhang, Li Li, Ge Sun, Zian Li, Zhongqi Ma, Yuchi Beihang Univ Sch Comp Sci & Engn State Key Lab Complex & Crit Software Environm Beijing Peoples R China Peking Univ Sch Comp Sci Beijing Peoples R China Huawei Cloud Comp Technol Co Ltd Shenzhen Peoples R China

ISBN: (数字)9798400712487

ISBN: (纸本)9798400712487

Providing personalized and timely feedback for student's programming assignments is useful for programming education. Automated program repair (APR) techniques have been used to fix the bugs in programming assignments, where the Large Language Models (LLMs) based approaches have shown promising results. Given the growing complexity of identifying and fixing bugs in advanced programming assignments, current fine-tuning strategies for APR are inadequate in guiding the LLM to identify bugs and make accurate edits during the generative repair process. Furthermore, the autoregressive decoding approach employed by the LLM could potentially impede the efficiency of the repair, thereby hindering the ability to provide timely feedback. To tackle these challenges, we propose FASTFIXER, an efficient and effective approach for programming assignment repair. To assist the LLM in accurately identifying and repairing bugs, we first propose a novel repair-oriented fine-tuning strategy, aiming to enhance the LLM's attention towards learning how to generate the necessary patch and its associated context. Furthermore, to speed up the patch generation, we propose an inference acceleration approach that is specifically tailored for the program repair task. the evaluation results demonstrate that FASTFIXER obtains an overall improvement of 20.46% in assignment fixing when compared to the state-of-the-art baseline. Considering the repair efficiency, FASTFIXER achieves a remarkable inference speedup of 16.67x compared to the autoregressive decoding algorithm.

关键词： Automated Program Repair Large Language Models programming Education Inference Acceleration

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Fostering Student Competencies in Creative Project Work through STEAM and artificial Intelligence in Teaching art 4

Fostering Student Competencies in Creative Project Work Thro...

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4th international conference on Computation, Automation and Knowledge Management, ICCAKM 2023

作者： Kulanova, Fariza Baitureev, Daniyar Singh, Chandan Pal Nis Shymkent City Kazakhstan South Kazakhstan Pedagogical University Shymkent city Kazakhstan

ISBN: (纸本)9798350393248

the objective of this study is to determine if there exists a statistically significant distinction between high school pupils who get instruction in the topic of 'art' through the use of artificial Intelligence (AI) and those who receive instruction through the integration of science, Technology, engineering, arts, and Mathematics (STEAM). Creativity, particularly within the realm of art and design, is commonly perceived as the ability to think beyond conventional boundaries. the study posited an alternative perspective by equating computational thinking with creative thinking. A total of forty-two kids from a high school in Kazakhstan were chosen as case studies for the topic of art. the objective is to facilitate the integration of STEM education and art-based education. © 2023 IEEE.

关键词： artificial intelligence

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Orbital Analysis of Passive Dynamic Walker with Dual Arms 4

Orbital Analysis of Passive Dynamic Walker with Dual Arms

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4th international conference on Industrial Automation, Robotics and Control engineering, IARCE 2024

作者： Xie, Zhongqu Gong, Xue Li, Long Zhou, Shichao Wang, Yulin School of Mechanical Engineering Nanjing University of Science and Technology Nanjing China School of Art and Design Nanjing Institute of Technology Nanjing China School of Mechanical Engineering Taiyuan Institute of Technology Taiyuan China

ISBN: (纸本)9798350380323

Unlike traditional models that predominantly consider lower limb dynamics, this research introduces the study of the passive biped walking with dual arms. the system's dynamic movement is represented through an optimization formulation, subject to constraints that ensure periodicity and optimal behavior. With optimization, the passive walker could generate natural biped locomotion with dual arm swing motion. Next, the study extensively investigates the effects of the arm's dynamic parameters on the overall locomotion, stability, and energy demand of the entire system. the results clearly demonstrate the impact of these dynamic parameters on stability, energy consumption, and step characteristics of the biped locomotion. Moreover, the insights gained from this study provide valuable guidance for the future development of robotic systems that more closely mimic human locomotion. © 2024 IEEE.

关键词： Biped locomotion

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Innovative Outlier Detection with SEBOOST: A Novel Hybrid Ensemble Approach 4

Innovative Outlier Detection with SEBOOST: A Novel Hybrid En...

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4th IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Son, Nguyen Khanh Sangaiah, Arun Kumar Medhane, Darshan Vishwasrao International Graduate School Of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan National Sun Yat-sen University Department of Computer Science and Engineering Kaohsiung Taiwan

ISBN: (纸本)9798350352931

Outlier detection is critical in numerous domains such as financial fraud detection, cybersecurity, and system surveillance. this study introduces SEBOOST, a novel hybrid ensemble algorithm that combines supervised and unsupervised machine learning techniques for improved outlier detection. the method leverages sequential ensembles to generate enriched feature spaces, applies feature selection using information gain to remove irrelevant features, and employs a supervised CATBoost classifier for final predictions. SEBOOST was rigorously tested on 21 real-world datasets, demonstrating its superior performance over state-of-the-art methods with higher precision, true positive rate, and AUC-ROC scores. these results indicate that SEBOOST as a robust and scalable framework for addressing outlier detection challenges across diverse applications. © 2024 IEEE.

关键词： Self-supervised learning

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Diabetic and Hypertensive Retinopathy Classification from Retinal Images Using Dual Vision Transformer 4

Diabetic and Hypertensive Retinopathy Classification from Re...

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4th international conference on Digital Data Processing, DDP 2024

作者： Koduri, Harsha Ma, Ming Katz School of Science and Health Yeshiva University New York United States Katz School of Science and Health Yeshiva University Department of Graduate Computer Science and Engineering New York United States

ISBN: (纸本)9798331515706

Accurate and automated classification of diabetic and hypertensive retinopathy conditions plays an important role in the diagnosis as it can lead to severe vision loss if not promptly diagnosed and treated. We propose to use a multi-scale method via dual vision transformer to improve the detection and classification of these retinopathy conditions. the method is capable of extracting more holistic global view of semantic feature and learning finer local features. To demonstrate the effectiveness of the method in this work, comparative experiments against the state-of-the-art methods are conducted. Experimental results show that the method in this work achieves better performance in terms of various metrics including accuracy, F1 score, precision, and recall. this work underscores the potential of employing multi-scale dual vision transformer method to enhance the reliability and precision of automated retinal disease classification, offering significant benefits for early diagnosis and patient care. © 2024 IEEE.

关键词： classification diabetic retinopathy hypertensive retinopathy multi-scale retinal images vision transformer

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