检索结果-内蒙古大学图书馆

Reversible data hiding based on pixel segmentation and prediction for improving hiding capacity

Signal, Image and Video Processing 2024年第6-7期18卷 5043-5059页

作者： Huang, Cheng-Ta Weng, Chi-Yao Zheng, Guo-Ting Weng, Hao-Yu International Bachelor Program in Informatics Yuan Ze University Taoyuan320315 Taiwan Department of Information Management Yuan Ze University Taoyuan320315 Taiwan Department of Computer Science and Information Engineering National Chiayi University Chiayi City600355 Taiwan Department of Computer Science and Artificial Intelligence National Pingtung University Pingtung900391 Taiwan Department of Computer Science and Information Engineering National Central University Taoyuan City320317 Taiwan

The idea of the traditional histogram shifting technique is to hide a message within the cover-image pixel distribution. However, the embedding capacity is limited by the peak point occurrences. To solve this problem, many reversible data hiding (RDH) techniques are developed to combine pixel prediction and histogram shifting to increase the embedding payload. In this paper, a RDH scheme based on chessboard prediction and pixel partitioning is proposed for enhancing the hiding payload. Pixel partitioning is used to separate a pixel into two parts: most significant bit and least significant bit. Then, the chessboard predictor is adopted to generate prediction errors and separate the pixels. Thereafter, histogram shifting is applied to embed the secret messages into prediction errors. Due to the high similarity of adjacent pixels in an image, the generated prediction errors are highly close to zero, resulting in a large embedding space. The experimental results indicate that the proposed method has superior performance in embedding capacity with acceptable image visual quality compared to existing schemes since it adopts the adaptive adjustment strategy to achieve this improved balance. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Pixels

来源：评论

学校读者我要写书评

暂无评论

Identification of Software Bugs by Analyzing Natural Language-Based Requirements Using Optimized Deep Learning Features

引用

computers, Materials & Continua 2024年第3期78卷 4379-4397页

作者： Qazi Mazhar ul Haq Fahim Arif Khursheed Aurangzeb Noor ul Ain Javed Ali Khan Saddaf Rubab Muhammad Shahid Anwar Department of Computer Science and Engineering and International Bachelor’s Program in Informatics Yuan Ze UniversityZhongliTaoyuan320315Taiwan Department of Computer Science National University of PakistanRawalpindiPunjab46000Pakistan Department of Computer Software Engineering MCSNational University of Sciences and TechnologyIslamabad44000Pakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Department of Computer Science School of PhysicsEngineering and Computer ScienceUniversity of HertfordshireHatfieldAL109ABUK Department of Computer Engineering College of Computing and InformaticsUniversity of SharjahSharjah27272United Arab Emirates Department of AI and Software Gachon UniversitySeongnam-SI13120South Korea

Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty *** are exploring machine learning to predict software bugs,but a more precise and general approach is *** bug prediction is crucial for software evolution and user training,prompting an investigation into deep and ensemble learning ***,these studies are not generalized and efficient when extended to other ***,this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification *** methods involved feature selection,which is used to reduce the dimensionality and redundancy of features and select only the relevant ones;transfer learning is used to train and test the model on different datasets to analyze how much of the learning is passed to other datasets,and ensemble method is utilized to explore the increase in performance upon combining multiple classifiers in a *** National Aeronautics and Space Administration(NASA)and four Promise datasets are used in the study,showing an increase in the model’s performance by providing better Area Under the Receiver Operating Characteristic Curve(AUC-ROC)values when different classifiers were *** reveals that using an amalgam of techniques such as those used in this study,feature selection,transfer learning,and ensemble methods prove helpful in optimizing the software bug prediction models and providing high-performing,useful end mode.

关键词： Natural language processing software bug prediction transfer learning ensemble learning feature selection

来源：评论

学校读者我要写书评

暂无评论

Practical Implementation of Q-Learning and Object Detection for Mobile Robot Path Planning

Practical Implementation of Q-Learning and Object Detection ...

引用

2024 international Conference on Advanced Robotics and Intelligent Systems, ARIS 2024

作者： Valcourt, John P. Chandler, Franya M. Avrelus, Chamma Lee, Jou-Yi Güllü, Ali Ihsan Shah, Syed H. Yuan Ze University Dept. of Computer Science and Engineering Taoyuan Taiwan Yuan Ze University International Bachelor Program in Ece Taoyuan Taiwan Yuan Ze University Mechanicai/Industrial Engineering Taoyuan Taiwan Yuan Ze University International Bachelor Program in Engineering Taoyuan Taiwan Gebze Technical University Defense Electronics Engineering Kocaeli Turkey

ISBN: (纸本)9798350362572

Expanding on an earlier study that assessed the performance of a Q-learning approach for solving the path planning problem for mobile robots, this research implemented the RL approach in a real-world setting employing the RoboMas-ter EP Core. The robotic system also included object detection and recognition through the robot's sensors and a pre-trained YOLOv9 model. The robot navigated to predefined target points while avoiding stationary obstacles. The Q-learning algorithm was trained using the Google Colaboratory platform. Experi-ments conducted at various speeds identified an optimal speed and high success rates in obstacle avoidance and target region accuracy were achieved. Additionally, the object detection sys-tem demonstrated strong performance in real-time applications. Despite these successes, challenges such as high friction and multitasking inefficiencies were identified. Future research should address these limitations by enhancing control systems and the robot's multitasking capabilities, as well as using a computer with better processing power to improve overall system performance further. © 2024 IEEE.

关键词： Robot programming

来源：评论

学校读者我要写书评

暂无评论

Performance Evaluation of Oversampling Methods on Deep Learning-Based Skin Cancer Classification

Performance Evaluation of Oversampling Methods on Deep Learn...

引用

2023 international Seminar on Application for Technology of Information and Communication, iSemantic 2023

作者： Supriyanto, Catur Salam, Abu Zeniarja, Junta Wijaya, Adi Universitas Dian Nuswantoro Bachelor Program in Informatics Engineering Faculty of Computer Science Semarang Indonesia Universitas Indonesia Maju Department of Health Information Management Jakarta Indonesia

ISBN: (纸本)9798350339215

The field of dermatology faces considerable challenges when it comes to early detection of skin cancer. Our study focused on using different datasets, including original data, augmented data, and SMOTE oversampled data, to identify skin cancer. Our dataset consisted of images of skin lesions from the MNIST Skin Cancer dataset (HAM 10000), including samples of both cancerous and benign cases in the dataset. We employed data augmentation to expand the dataset's size and increase the diversity of skin lesion features. Furthermore, to tackle class imbalance in the dataset, we applied the SMOTE oversampling technique to generate synthetic samples for the under-represented group. With the original, augmented, and SMOTE oversampled datasets, we trained a Convolutional Neural Network (CNN) model. The performance of the model was evaluated using accuracy, recall, precision, and F1-score. The comparison between the results obtained from the original data, augmented data, and SMOTE oversampling data clearly revealed distinctions in performance. Our findings clearly demonstrate that employing data augmentation and SMOTE oversampling can significantly enhance the efficacy of skin cancer detection. © 2023 IEEE.

关键词： Neural network models

来源：评论

学校读者我要写书评

暂无评论

EUDRL: Explainable Uncertainty-Based Deep Reinforcement Learning for Portfolio Management 15

EUDRL: Explainable Uncertainty-Based Deep Reinforcement Lear...

引用

15th international Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Lina, Jia Banda, Shahab S. Paib, Hao-Ting Rawal, Bharat S. International Graduate School of Artificial Intelligence Yunlin County Douliu City Taiwan Bachelor Program of Big Data Applications National Pingtung University Pingtung Taiwan Department of Computer Science and Digital Technologies University GgramblingLA United States

ISBN: (纸本)9798350370249

Supervised deep learning (SDL) has shown remarkable success in various financial applications, such as stock prediction and fraud detection. However, SDL's reliance on class labels renders it unsuitable for portfolio management (PM) tasks, where such labels are often unavailable. To address this limitation, we propose a novel two-level architecture based on deep reinforcement learning (DRL) for PM, which does not require class labels. Our approach comprises several local agents that provide trading decisions and uncertainty assessments for individual stocks, and a global agent that makes portfolio management decisions based on the outputs of the local agents. Additionally, we incorporate the concept of explainable AI (XAI) into our framework using the SHAP (Shapley additive explanations) method, enhancing the transparency and interpretability of the global agent's decisions. Our experimental results demonstrate that the proposed architecture consistently yields profitable outcomes in the market. ©2024 IEEE.

关键词： Deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

YNote: A Novel Music Notation for Fine-Tuning LLMs in Music Generation

arXiv

引用

arXiv 2025年

作者： Lu, Shao-Chien Yeh, Chen-Chen Cho, Hui-Lin Hsu, Chun-Chieh Hsu, Tsai-Ling Wu, Cheng-Han Shih, Timothy K. Lin, Yu-Cheng Department of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan International Bachelor Program in Informatics Yuan Ze University Taoyuan Taiwan Department of Computer Science and Information Engineering National Central University Taoyuan Taiwan

The field of music generation using Large Language Models (LLMs) is evolving rapidly, yet existing music notation systems, such as MIDI, ABC Notation, and MusicXML, remain too complex for effective fine-tuning of LLMs. These formats are difficult for both machines and humans to interpret due to their variability and intricate structure. To address these challenges, we introduce YNote, a simplified music notation system that uses only four characters to represent a note and its pitch. YNote’s fixed format ensures consistency, making it easy to read and more suitable for fine-tuning LLMs. In our experiments, we fine-tuned GPT-2 (124M) on a YNote-encoded dataset and achieved BLEU and ROUGE scores of 0.883 and 0.766, respectively. With just two notes as prompts, the model was able to generate coherent and stylistically relevant music. We believe YNote offers a practical alternative to existing music notations for machine learning applications and has the potential to significantly enhance the quality of music generation using LLMs. Copyright © 2025, The Authors. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Lightweight Authenticated Key Agreement Protocol for Smart Power Grid Systems Using PUF

IEEE Open Journal of the Communications Society

引用

IEEE Open Journal of the Communications Society 2024年 5卷 3568-3580页

作者： Zahoor, Amina Mahmood, Khalid Saleem, Muhammad Asad Badar, Hafiz Muhammad Sanaullah Le, Tuan-Vinh Das, Ashok Kumar Department of Computer Science Sahiwal57000 Pakistan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Emerson University Multan Department of Emerging Computing Technologies Multan61000 Pakistan Fu Jen Catholic University Bachelor's Program of Artificial Intelligence and Information Security New Taipei City24206 Taiwan International Institute of Information Technology Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India

The Smart Power Grid (SPG) is pivotal in orchestrating and managing demand response in contemporary smart cities, leveraging the prowess of Information and Communication Technologies (ICTs). Within the immersive SPG environment, the ubiquitous deployment of smart meters stands as a testament to their paramount importance in the realm of vigilant monitoring and oversight. These smart meters are installed on high-tension electricity lines and transmit information about electricity outages and other issues to the utility center. To access services from utility centers, smart meters need to communicate securely over a public channel, even though the network itself is insecure. However, potential attacks from adversaries ( Ad) can exploit this communication. Therefore, protecting this communication is of utmost importance. Several privacy-preserving authentication protocols designed for SPG have been introduced in the literature. Nevertheless, a significant number of these protocols exhibit vulnerabilities to diverse security attacks. This article introduces a lightweight and anonymous authentication protocol specifically designed to address these concerns in the SPG environment. Our protocol ensures both security and efficiency, surpassing other comparable protocols in terms of its lightweight nature. By employing both formal and informal analysis, we showcase the robustness of our protocol against significant attacks while remaining lightweight. The proposed protocol provides added security features and incurs 23.8879% lower computation cost than related protocols. Consequently, our protocol is highly suitable for implementation in the SPG system. © 2020 IEEE.

关键词： Hash functions

来源：评论

学校读者我要写书评

暂无评论

High-Capacity Double-Layer Data Hiding Technique Based on 3D Magic Cube 25th

High-Capacity Double-Layer Data Hiding Technique Based on 3D...

引用

25th international computer Symposium on New Trends in computer Technologies and Applications, ICS 2022

作者： Weng, Hao-Yu Tsai, Min-Yi Wang, Wei-Jen Huang, Cheng-Ta Department of Computer Science and Information Engineering National Central University Taoyuan City320 Taiwan Department of Information Management Yuan Ze University Taoyuan City320 Taiwan International Bachelor Program in Informatics Yuan Ze University Taoyuan City320 Taiwan

ISBN: (纸本)9789811995811

Information technology and the Internet have progressed rapidly in people’s lives, the privacy of information has become an important issue due to the accessibility of data. Therefore, to enhance information security and privacy protection, this paper proposes a new high-capacity method for data hiding. The proposed method is based on 3D magic cube with double layers embedding. In the field of data hiding, the reference matrix-based methods attract many researchers because of its high-capacity. Recently, as the needs of the high payloads of the stego-image, the multi-layers-based technique is applied to provide larger embedding capacity. However, the image after data embedding will cause serious distortions, especially on multi-layers embedding, hence, keeping the image at a good visual quality and achieving high-payloads are the main challenges for data hiding. In this scheme, we take advantage of 3D magic cube and 3D reference space with double layers embedding for our data hiding technique to improve the embedding payloads. The experimental results show that our method not only provides good embedding capacity (4.00 bpp) but also keeps the image at acceptable quality (In terms of PSNR > 30). © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

Bloom-epistemic and sentiment analysis hierarchical classification in course discussion forums

arXiv

引用

arXiv 2024年

作者： Toba, Hapnes Hernita, Yolanda Trixie Ayub, Mewati Wijanto, Maresha Caroline Master of Computer Science Study Program Faculty of Information Technology Maranatha Christian University Bandung Indonesia Bachelor of Informatics Study Program Faculty of Information Technology Maranatha Christian University Bandung Indonesia

Online discussion forums are widely used for active textual interaction between lecturers and students, and to see how the students have progressed in a learning process. The objective of this study is to compare appropriate machine-learning models to assess sentiments and Bloom's epistemic taxonomy based on textual comments in educational discussion forums. The proposed method is called the hierarchical approach of Bloom-Epistemic and Sentiment Analysis (BE-Sent). The research methodology consists of three main steps. The first step is the data collection from the internal discussion forum and YouTube comments of a Web programming channel. The next step is text preprocessing to annotate the text and clear unimportant words. Furthermore, with the text dataset that has been successfully cleaned, sentiment analysis and epistemic categorization will be done in each sentence of the text. Sentiment analysis is divided into three categories: positive, negative, and neutral. Bloom's epistemic is divided into six categories: remembering, understanding, applying, analyzing, evaluating, and creating. This research has succeeded in producing a course learning subsystem that assesses opinions based on text reviews of discussion forums according to the category of sentiment and epistemic analysis. © 2024, CC BY-SA.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Cluster Dominance Analysis of Strength Training Motion Characteristics

Cluster Dominance Analysis of Strength Training Motion Chara...

引用

IEEE Global Conference on Consumer Electronics (GCCE)

作者： Hapnes Toba Elizabeth Wianto Maya Malinda Agung Wijaya Al Halim Chien-Hsu Chen Master Program in Computer Science Universitas Kristen Maranatha Bandung Indonesia Bachelor Program in Visual Communication Design Universitas Kristen Maranatha Bandung Indonesia Bachelor Program in Management Universitas Kristen Maranatha Bandung Indonesia Bachelor Program in Informatics Universitas Kristen Maranatha Bandung Indonesia Industrial Design Department National Cheng Kung University Tainan Taiwan

This paper presents an approach to analyze clusters as a means to determine the characteristics of strength training motion patterns. The proposed method emphasizes the observation of dominance sequences within clusters and is reinforced by the formation of specific characteristics within each cluster. Data collection is performed using video-guided strength training exercises equipped with 1 kg dumbbells and recorded by a sensor embedded in smartwatches. The analysis method involves applying the concept of density affinity, which calculates the density ratio of clusters to the recognized motions. Subsequently, the dominance sequence is observed to identify which clusters exhibit distinct characteristics, ultimately determining the intended motions. The research findings demonstrate the potential for further investigation into a more comprehensive understanding of motion patterns, leading to the development of models that can be integrated into mobile devices or smartwatches.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：