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Digital Media Image Synthesis System Based on Network Intelligence 2

Digital Media Image Synthesis System Based on Network Intell...

引用

2nd IEEE international conference on Data Science and Network Security, ICDSNS 2024

作者： Ma, Shiming School of Information Engineering Nanning University Guangxi Nanning China

ISBN: (纸本)9798350373110

At present, the rapid development of artificial intelligence (AI) and deep learning, and the progress of these information technologies have greatly improved the quality of image synthesis, meeting the needs of modern society for highquality visual content. This approach not only drives advances in digital media technology, but also provides new tools and possibilities for the entertainment, education and healthcare industries. Therefore, this paper mainly describes the research of digital media image synthesis system based on network intelligence, and focuses on network intelligence technology, digital media image synthesis technology, data-driven image synthesis, image style conversion based on deep learning, and automatic image restoration based on AI. Finally, the results of two sets of simulation experiments are as follows: the clarity and realism scores of the optimized synthesis system after image processing are increased by 9.6 points, and the operation efficiency of the image synthesis task is increased by 7.4%, and the resource consumption is reduced by 12.2%. © 2024 IEEE.

关键词： digital media image synthesis network intelligence system research

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Using Tandem Neural Networks to Observe Jones Matrix Parameters

Using Tandem Neural Networks to Observe Jones Matrix Paramet...

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2024 IEEE international conference on Computational Electromagnetics, ICCEM 2024

作者： Liu, Cong Yuan, Yueyi Wang, Yuxiang Zhang, Kuang School of Electronics and Information Engineering Harbin Institude of Technology Harbin China

ISBN: (纸本)9798350383317

Deep learning, with its powerful computing and processing capabilities for data, is increasingly being applied in the design and regulation of metasurface and other tasks. This article proposes a method for freely adjusting the eight degrees of freedom of the Jones matrix through deep learning. Using tandem neural networks (TNN), reverse design of metasurface units for different Jones matrices to achieve polarization control. The proposed method can greatly accelerate the implementation of polarization control and metasurface design. © 2024 IEEE.

关键词： Degrees of freedom (mechanics)

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The Creation of Mnemonics in the Virtual teaching of Mathematics to engineering Students 4

The Creation of Mnemonics in the Virtual Teaching of Mathema...

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4th IEEE international conference on Advanced learning Technologies for Education and Research, ICALTER 2024

作者： Henry J., Javier Ninahuaman Grimaldo, Quispe Santivañez Rodriguez Cangalaya, Nora Miriam Z., Espinoza Veliz Universidad Nacional Autónoma Altoandina de Tarma Facultad de Ingeniería Tarma Peru Universidad Nacional Autónoma Altoandina de Tarma Facultad de Enfermería Tarma Peru

ISBN: (纸本)9798331510244

Mathematics is a real challenge for many students and virtuality is often frowned upon by the student due to the different teacher-student interaction. The study aims to test the effectiveness of the creation of mnemonics in the virtual teaching of Mathematics to future engineers. The different approach consisted of commissioning the students to create the mnemonics. Through a study with a quantitative approach, applied type, explanatory level and quasi-experimental design, with pre- and post-test, an experimental group and a control group, this study applied exams evaluated by means of rubrics to a sample of 26 students. The analyses were made using statistical tables and the Student's t statistic for the hypothesis test. The results show superiority of the stimulus (sig of 0.014): creation of mnemonics;and highlight the use of cognitive and virtual techniques during and after the Covid 19 pandemic, in addition to contributing to collaborative work and providing a great tool for teaching as it allows us to know if the creation of mnemonics influences the learning of Mathematics. © 2024 IEEE.

关键词： Students

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Cross-Lingual Transfer learning in RNNs for Enhancing Linguistic Diversity in Natural Language Processing

Cross-Lingual Transfer Learning in RNNs for Enhancing Lingui...

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2024 international conference on Advances in computing Research on Science engineering and Technology, ACROSET 2024

作者： Tiwari, Shikha Babu, Ch. Meher Shanker, P. Shahnaz, K.V. Roy, Vandana Kashyap, Ramgopal Amity University Raipur India Potti SriRamulu Chalavadi Mallikarkuna Rao College of Engineering and Technology Vijayawada India Cvr College of Engineering Dept. of Cse Hyderabad India Veltech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Dept. of Ece Chennai India Ggits DoEC Jabalpur India Amity University Chhattisgarh India

ISBN: (纸本)9798350388800

In addition to removing biases, these findings reveal a comprehensive method for improving cross-lingual transfer learning in NLP, which in turn makes language more diverse. This approach was created as a result of this effort. In order to construct the system utilizing these methodologies, five fundamental procedures are followed. One approach that falls under this category is MTI, which stands for Multilingual Embedding Adaptation and is used for Machine Translation-based Initialization. Ethical Bias Mitigation, Linguistic Feature Alignment, and Recurrent Neural Network Adaptation are some supplementary methods. Developing each strategy to address the wide variety of issues that may arise during cross-lingual transfer learning required considerable deliberation. Starting with translated data, the MTI algorithm aligns the source and target language embeddings, the RNA algorithm aligns RNN structures, the LFA algorithm aligns linguistic characteristics, and the EBM method lowers biases fairly. The desired outcomes are attained by employing all these techniques. The outcomes of ablation research demonstrated the significance of each approach and their interdependence in ensuring the system's functionality. The framework provides a comprehensive strategy for increasing the linguistic diversity of natural language processing (NLP) models to accommodate individuals from varying cultural backgrounds and language abilities. Issues of social justice and equity receive substantial consideration within the framework. © 2024 IEEE.

关键词： Contrastive learning

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Machine learning Based Integrated Audio and Text Modalities for Enhanced Emotional Analysis 5

Machine Learning Based Integrated Audio and Text Modalities ...

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5th international conference on Inventive Research in computing Applications, ICIRCA 2023

作者： Agarkhed, Jayashree Vishalakshmi Pda College of Engineering Computer Science and Engineering Kalaburagi Karnataka585102 India

ISBN: (纸本)9798350321425

Emotions play a crucial role in human communication, and the ability to automatically recognize them has broad applications in various domains, including human-computer interaction, affective computing, and virtual reality. Bimodal speech emotion recognition which aims to accurately recognize and interpret emotions expressed in speech signals. This article reviews the state-of-the-art techniques and methodologies employed in multimodal speech emotion recognition, highlighting the advancements in feature extraction, fusion strategies, and machine learning algorithms. Additionally, emerging trends and future directions in this field are discussed, focusing on the challenges and opportunities for improving the performance and robustness of multimodal speech emotion recognition systems. © 2023 IEEE.

关键词： Feature extraction

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Prompt-based and Fine-tuned GPT Models for Context-Dependent and -Independent Deductive Coding in Social Annotation 24

Prompt-based and Fine-tuned GPT Models for Context-Dependent...

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14th Annual international conference on learning Analytics and Knowledge (LAK) - learning Analytics in the Age of Artificial Intelligence

作者： Hou, Chenyu Zhu, Gaoxia Zheng, Juan Zhang, Lishan Huang, Xiaoshan Zhong, Tianlong Li, Shan Du, Hanxiang Ker, Chin Lee Nanyang Technol Univ Singapore Singapore Nanyang Technol Univ Natl Inst Educ Singapore Singapore Lehigh Univ Bethlehem PA USA Cent China Normal Univ Wuhan Peoples R China McGill Univ Montreal PQ Canada Western Washington Univ Bellingham WA USA

ISBN: (纸本)9798400716188

GPT has demonstrated impressive capabilities in executing various natural language processing (NLP) and reasoning tasks, showcasing its potential for deductive coding in social annotations. This research explored the effectiveness of prompt engineering and fine-tuning approaches of GPT for deductive coding of contextdependent and context-independent dimensions. Coding contextdependent dimensions (i.e., Theorizing, Integration, Reflection) requires a contextualized understanding that connects the target comment with reading materials and previous comments, whereas coding context-independent dimensions (i.e., Appraisal, Questioning, Social, Curiosity, Surprise) relies more on the comment itself. Utilizing strategies such as prompt decomposition, multi-prompt learning, and a codebook-centered approach, we found that prompt engineering can achieve fair to substantial agreement with expertlabeled data across various coding dimensions. These results affirm GPT ' s potential for effective application in real-world coding tasks. Compared to context-independent coding, context-dependent dimensions had lower agreement with expert-labeled data. To enhance accuracy, GPT models were fine-tuned using 102 pieces of expert-labeled data, with an additional 102 cases used for validation. The fine-tuned models demonstrated substantial agreement with ground truth in context-independent dimensions and elevated the inter-rater reliability of context-dependent categories to moderate levels. This approach represents a promising path for significantly reducing human labor and time, especially with large unstructured datasets, without sacrificing the accuracy and reliability of deductive coding tasks in social annotation. The study marks a step toward optimizing and streamlining coding processes in social annotation. Our findings suggest the promise of using GPT to analyze qualitative data and provide detailed, immediate feedback for students to elicit deepening inquiries.

关键词： GPT Prompt engineering Fine-tuning Social Annotation ContextDependent deductive coding

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Detection of Barrett's Esophagus in Endoscopic Images Using Machine learning Algorithms

Detection of Barrett's Esophagus in Endoscopic Images Using ...

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2024 IEEE international conference on Communication, computing and Signal Processing, IICCCS 2024

作者： Al-Fatlawy, Ramy Riad Divya Khare, Saurabh Nitika Satyanarayana, A.N. Rajashree The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq The Islamic University Of Al Diwaniyah College Of Technical Engineering Department Of Computers Techniques Engineering Al Diwaniyah Iraq Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Ies University Department of Information Technology M.P. Bhopal India Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Mohali India Sreenidhi Institute of Science and Technology Ece Department Hyderabad India Department of Cse Nitte India

ISBN: (纸本)9798350390759

BE is a precursor to esophageal adenocarcinoma in which the patient is at a higher risk compared to those who the disease is not diagnosed. As for cancer, its early detection and proper handling should be of a significant concern to avoid chances of becoming CANCER. This paper aims at investigating the application and development of machine learning particularly Convolutional Neural Network (CNN) for automated identification of BE when seen in Endoscopy. Utilizing a mixture of open-source databases and specific clinical materials, a complex and well-established CNN model was created and calibrated to diagnose BE. Its efficacy was further supported by high performance rates such as the accuracy of 91% and sensitivity and specificity of 89% and 92% respectively along with AUC ROC value of 0. 94. To solve for the overfitting problem, data augmentation and transfer learning strategies were applied to improve the generalization of the model. Also, to increase the explainability of the deep-learning-based prediction model, certain explainable AI techniques were adopted. The model has been compared with the traditional ML model as well as the basic CNN, and the improved model seems to perform better. About the early detection of BE, this study reveals that the application of the ML-based tools shall provide new ideas and methods to help clinicians to detect BE at its early stage so as to improve the quality of patients' lives and the survival rate of death from esophageal adenocarcinoma. The clinical photographs generated by this software still require further fine-tuning and refinement to alleviate issues that hinder its implementation in clinical use and to integrate this software seamlessly in clinical settings. © 2024 IEEE.

关键词： Endoscopy

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An Exploratory Study on Upper-Level computing Students' Use of Large Language Models as Tools in a Semester-Long Project

An Exploratory Study on Upper-Level Computing Students' Use ...

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2024 ASEE Annual conference and Exposition

作者： Tanay, Ben Arie Arinze, Lexy Chiwete Joshi, Siddhant Sanjay Davis, Kirsten A. Davis, James C. Purdue Engineering Education United States Purdue University West Lafayette United States

Background: Large Language Models (LLMs) have begun to influence software engineering practice since the public release of GitHub's Copilot and OpenAI's ChatGPT in 2022. Tools built on LLM technology could revolutionize the way software engineering is practiced, offering interactive "assistants" that can answer questions and prototype software. It falls to software engineering educators to teach future software engineers how to use such tools well, by incorporating them into their pedagogy. While some institutions have banned ChatGPT, other institutions have opted to issue guidelines for its use. Additionally, researchers have proposed strategies to address potential issues in the educational and professional use of LLMs. As of yet, there have been few studies that report on the use of LLMs in the classroom. It is, therefore, important to evaluate students' perception of LLMs and possible ways of adapting the computing curriculum to these shifting paradigms. Purpose: The purpose of this study is to explore computing students' experiences and approaches to using LLMs during a semester-long software engineering project. We investigated the impacts of a low-cost intervention. While there have been studies on the use of LLMs in the classroom, there have been limited works on the use within a project-based course in the computing classroom. Our study helps fill this knowledge gap. Design/Method: We collected data from a senior-level software engineering course at Purdue University, a large public R1 university in the Midwest. This course uses a project-based learning (PBL) design with a semester-long team project. In Fall 2023, the students were required to use LLMs such as ChatGPT and Copilot as they completed their projects. A sample of these student teams were interviewed in the middle and at the end of the semester to understand: (1) how they used LLMs in their projects;and (2) whether and how their perspectives on LLMs changed over the course of the semester. We ana

关键词： Students

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Oral Cancer Detections and Classification Using Region Based Convolutional Neural Network 2

Oral Cancer Detections and Classification Using Region Based...

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2nd IEEE international conference on Trends in Quantum computing and Emerging Business Technologies, TQCEBT 2024

作者： Kumar, M Prema Ashfaq Ahmed, K. Subash, K. Aeron, Anurag Department of Ece Andhra Pradesh Bhimavaram India Department of Emerging Technologies in Computer Science Andhra Pradesh Kurnool India Department of Data Science Trichy India Miet Meerut Department of Computer Science and Engineering Meerut India

ISBN: (纸本)9798350384277

Early detection dramatically increases the survival rate of oral cancer (OC). Artificial intelligence (AI) technology has garnered more attention in the field of diagnostic medicine in present periods. This study set out to assess the available data regarding AI's efficacy in OC diagnosis critically. Artificial intelligence diagnostic accuracy and capacity to detect early phases of OC were highlighted. In this project, performance indicators will be measured and oral cancer will be divided and classified using intelligent computing techniques. The role of oral cancer classification and detection to achieve a high recognition rate while leveraging the best theoretical components of oral cancer images, a newly established region-based Convolutional Neural Network (RCNN-COA) and the Chimp Optimization Algorithm were used to improve a Deep learning Method. Then, using the recommended Chimp Optimization Algorithm (COA), a region-based convolutional neural network (R-CNN) classifier was trained using the acquired theoretical properties and the innovative image. A comparison of many deep learning and machine learning models' performances has been reported in a study. The findings imply that the deep learning model is capable of managing the problematic task of early oral malignant tumor detection. © 2024 IEEE.

关键词： Contrastive learning

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Personalization and Smartphone Implementation of ADRO Hearing Aid Amplification by Bayesian Machine learning 21

Personalization and Smartphone Implementation of ADRO Hearin...

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21st international conference on Electrical engineering, computing Science and Automatic Control, CCE 2024

作者： Tasnim, Nafisa Zarrin Ni, Aoxin Lobarinas, Edward Kehtarnavaz, Nasser University of Texas at Dallas Department of Electrical and Computer Engineering Richardson United States Callier Center for Communication Disorders University of Texas at Dallas Richardson United States

ISBN: (纸本)9798350377545

The amplification function of hearing aids is normally prescribed based on population-based prescriptions, often overlooking individual hearing needs. Previous studies have shown that personalization or individualization of prescriptive amplification can lead to improved hearing. This paper introduces a personalized amplification strategy that applies a Bayesian learning personalization algorithm to the Adaptive Dynamic Range Optimization (ADRO) prescription. The personalized ADRO amplification strategy is then implemented on a smartphone platform as an app that can be deployed in the field or in real-world audio environments. The results of hearing preference experiments from six participants with hearing loss are reported. The results, on average, indicate higher preference for the personalized ADRO relative to the standard ADRO prescription. © 2024 IEEE.

关键词： Smartphones

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