检索结果-内蒙古大学图书馆

14th IEEE International Conference on Control System, Computing and engineering, ICCSCE 2024

作者： Ajibade, Samuel-Soma M. Issa, Bayan Jasser, Muhammed Basheer Hassan, Farrukh Aldharhani, Ghassan Saleh Al-Hadi, Ismail Ahmed Al-Qasem Akintoye, Kayode Akinlekan Tabuena, Almighty Cortezo School of Engineering and Technology Sunway University Department of Computing and Information Systems No. 5 Jalan Universiti Selangor Darul Ehsan Bandar Sunway47500 Malaysia Istanbul Ticaret University Department of Computer Engineering Istanbul Turkey University of Aleppo Faculty of Informatics Engineering Syria Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur Malaysia UKM University Faculty of Information Science and Technology Selangor Bangi Malaysia The Federal Polytechnic Department of Computer Science Ado Ekiti Ekiti State Nigeria University of the City of Valenzuela College of Arts and Sciences Philippines

ISBN: (纸本)9798350364507

This study examines the academic field of Artificial Intelligence in Education (AI-ED) by examining the publications and indexed documents in Scopus from 2010 to 2023. Consequently, we examined the publishing trends, exemplary articles, and the most engaged participants and funding institutions in the domain of AI-ED research. A bibliometric analysis was performed to examine the network of co-authorships, keywords, and citations in the domain of AIED research. The Scopus search resulted in the discovery of 10,474 published documents on AI-ED. Additionally, trends analysis indicated a significant increase of over 3,000% in publications between 2010 and 2023. Conference papers are the most often utilized document type, including 6757 publications or 64.5% of the overall amount. The ACM International Conference Proceeding Series is the primary source for published materials, accounting for 675 publications or 6.44% of the total publications on the topic. The most prolific researchers in the field of AI-ED are Breazeal, C and Chai C.S, each having published 18 documents. Additionally, the two institutions that have produced the highest number of published documents are Carnegie Mellon University (CMU) and the University of NC State University (NCSU). The output of the affiliations active in the topic is primarily attributed to the funders, specifically the National Science Foundation, which is the leading funding institution based in the United States and has contributed to 354 publications. An analysis of keyword co-occurrence revealed 6 main study areas that cover the essential tools, theories, methodologies, and socioeconomic and financial aspects of artificial intelligence in education (AIED). Future research in the field of artificial intelligence in education will focus on employing sophisticated deep learning, machine learning, and neural network algorithms to precisely predict students' learning patterns. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

A Triple-Channel Encrypted Hybrid Fusion Technique to Improve Security of Medical Images

引用

computers, Materials & Continua 2021年第7期68卷 431-446页

作者： Ahmed S.Salama Mohamed Amr Mokhtar Mazhar B.Tayel Esraa Eldesouky Ahmed Ali Future University in Egypt CairoEgypt Cairo Higher Institute for Engineering Computer Science and ManagementCairoEgypt Department of Electrical Engineering Faculty of EngineeringAlexandria UniversityAlexandria21544Egypt Department of Computer Science Faculty of Computers and InformaticsSuez Canal UniversityIsmailia CityEgypt Higher Future Institute for Specialized Technological Studies CairoEgypt Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia

Assuring medical images protection and robustness is a compulsory necessity *** this paper,a novel technique is proposed that fuses the wavelet-induced multi-resolution decomposition of the Discrete Wavelet Transform(DWT)with the energy compaction of the Discrete Wavelet Transform(DCT).The multi-level Encryption-based Hybrid Fusion Technique(EbhFT)aims to achieve great advances in terms of imperceptibility and security of medical images.A DWT disintegrated sub-band of a cover image is reformed simultaneously using the DCT ***,a 64-bit hex key is employed to encrypt the host image as well as participate in the second key creation process to encode the ***,a PN-sequence key is formed along with a supplementary key in the third layer of the ***,the watermarked image is generated by enclosing both keys into DWT and DCT *** fusions ability of the proposed EbHFT technique makes the best use of the distinct privileges of using both DWT and DCT *** order to validate the proposed technique,a standard dataset of medical images is *** results show higher performance of the visual quality(i.e.,57.65)for the watermarked forms of all types of medical *** addition,EbHFT robustness outperforms an existing scheme tested for the same dataset in terms of Normalized Correlation(NC).Finally,extra protection for digital images from against illegal replicating and unapproved tampering using the proposed technique.

关键词： Medical image processing digital image watermarking discrete wavelet transforms discrete cosine transform encryption image fusion hybrid fusion technique

来源：评论

学校读者我要写书评

暂无评论

Eye State Classification Using Ensemble Machine Learning Models and SMOTE on EEG Data

Eye State Classification Using Ensemble Machine Learning Mod...

引用

South Eastern European Design Automation, computer engineering, computer Networks and Social Media Conference (SEEDA-CECNSM)

作者： Maria Trigka Elias Dritsas Phivos Mylonas Industrial Systems Institute Athena Research and Innovation Center Patras Greece Department of Informatics and Computer Engineering University of West Attica Egaleo Greece

ISBN: (数字)9798331504489

ISBN: (纸本)9798331504496

Electroencephalography (EEG) data presents complex and high-dimensional signals, offering great potential for applications in various fields such as neurofeedback, clinical diagnostics, cognitive neuroscience, human-computer interaction (HCI), and beyond. Analyzing EEG signals requires expertise not only from neuroscience, but also from signal processing, machine learning (ML), and statistics to extract meaningful information from brain activity recordings. Specifically, the combination of EEG and ML can provide an advantage in addressing challenging classification tasks in these fields. The present study focuses on the classification of eye state (open or closed) using Ensemble ML models such as Random Forest (RF), Gradient Boosting (GB), AdaBoost, XGBoost, and LightGBM on EEG data. We apply the Synthetic Minority Over-sampling Technique (SMOTE) to address the class imbalance and conduct a comparative analysis of the models’ performance with and without SMOTE using 10-fold cross-validation across several metrics namely, Accuracy, Precision, Recall, F1-score, and the Area Under the Curve (AUC). The experimental results highlight the importance of addressing the class imbalance in EEG data to improve model performance.

关键词： Measurement Analytical models Accuracy Social networking (online) Brain modeling Boosting Electroencephalography Data models Time-domain analysis Random forests

来源：评论

学校读者我要写书评

暂无评论

Performance and Security Analysis of the Diffie-Hellman Key Exchange Protocol

Performance and Security Analysis of the Diffie-Hellman Key ...

引用

International Workshop on Semantic Media Adaptation and Personalization (SMAP)

作者： Elias Dritsas Maria Trigka Phivos Mylonas Industrial Systems Institute Athena Research and Innovation Center Patras Greece Department of Informatics and Computer Engineering University of West Attica Egaleo Greece

ISBN: (数字)9798331504502

ISBN: (纸本)9798331504519

The Diffie-Hellman Key Exchange Protocol (DHKE) is a fundamental element of modern cryptographic systems, enabling secure key exchange over unsecured channels. The present research work aims to provide a comprehensive analysis of DHKE, with a dual focus on performance and security. We examine the protocol’s robustness against classical and emerging threats, particularly in the context of the Discrete Logarithm Problem (DLP) and the advent of quantum computing. Performance is evaluated by measuring execution times for key lengths of 512 and 1024 bits, each recorded over 100 iterations. The results are analyzed to provide insights into both computational efficiency and variability, with graphical representations to illustrate the findings. The study underscores the balance between security and performance in DHKE and offers recommendations for future enhancements to maintain cryptographic resilience in the face of advancing computational capabilities.

关键词： Performance evaluation Protocols Quantum computing Parallel processing Length measurement Software Robustness Computational efficiency Cryptography Resilience

来源：评论

学校读者我要写书评

暂无评论

Sustainable Robot Future: A Speculative Design about Humanity, Robots, and Ecology 25

Sustainable Robot Future: A Speculative Design about Humanit...

引用

Proceedings of the 2025 Conference on Creativity and Cognition

作者： Lingyun Chen Zitao Zhang Muwu Shan Qing Xiao Eli Blevis Luddy School of Informatics Computing and Engineering Indiana University Bloomington Bloomington Indiana USA Human-Computer Interaction Institute Carnegie Mellon University Pittsburgh Pennsylvania USA Luddy School of Informatics Computing and Engineering Indiana University Bloomington Indiana USA

ISBN: (纸本)9798400712890

Robotics has emerged as a critical field of technological innovation. However, current design paradigms, rooted in industrial-era models, often prioritize centralized control, planned obsolescence, and rigid, one-size-fits-all solutions, undermining adaptability, sustainability, and personal autonomy. To address these limitations, we propose a speculative robotic design framework rooted in Sustainability, Adaptability, and Modularity. Our framework envisions robots as modular systems that can be assembled, reconfigured, and personalized by people, shifting design control away from centralized decision-makers and enabling long-term usability. Modularity enables adaptability and reduces obsolescence, while adaptability reinforces sustainability through circular resource use and extended lifecycles. This speculative framework not only provides a technical vision but also reimagines robotic design as a participatory and collaborative process aligned with ecological responsibility. Our speculative framework offers a manifesto for systemic change, reshaping robotics for a more equitable, sustainable future between humans, robots, and ecology.

关键词： Human-robot Interaction

来源：评论

学校读者我要写书评

暂无评论

Advancing general robotic manipulation with multimodal foundation models: An embodied AI paradigm

引用

Science China(Technological Sciences) 2025年第5期68卷 290-292页

作者： Shifeng HUANG He WANG Xing ZHOU Wenkai CHEN Haibin YANG Jianwei ZHANG Department of Mechanical and Aerospace Engineering Hong Kong University of Science and TechnologyHong Kong 999077China Foshan Institute of Intelligent Equipment Technology Foshan 528234China Foshan Huashu Robot Co. Ltd.Foshan 528234China Center on Frontiers of Computing Studies(CFCS) School of Computer SciencePeking UniversityBeijing 100871China Beijing Academy of Artificial Intelligence Beijing 100084China Department of Informatics Universität HamburgHamburg 20146Germany

Can current robotic technologies truly replicate the full scope and intricacies of human labour?In practice,the adoption of robots remains limited,especially in open,unstructured environments commonly encountered in everyday scenarios such as services,healthcare,agriculture,construction,and numerous other *** the perspective of general robotic manipulation,the challenges arise from three factors.(1)High operational barriers:human operators are obliged to master specialized robotic programming languages and gain a deep understanding of the tasks at *** tasks need to be broken down into action-level robotic programs,which results in high labour costs.(2)Limited autonomous task execution:robots lack the capability to independently plan and execute actions required to achieve the target *** limitation renders them unsuitable for deployment in open,unstructured environments that demand sophisticated interaction and seamless collaboration with humans.

关键词： multimodal foundation models autonomous task execution robotic manipulationthe general robotic manipulation robotic programming language embodied AI operational barriers robotic technologies

来源：评论

学校读者我要写书评

暂无评论

Resource Efficient Federated Learning for Deep Anomaly Detection in Industrial IoT applications

Resource Efficient Federated Learning for Deep Anomaly Detec...

引用

International Conference on Digital Signal Processing (DSP)

作者： Alexandros Gkillas Aris Lalos Athena Research Center Industrial Systems Institute Greece Dept. of Computer Engineering & Informatics University of Patras

Anomaly data constitute a thorny problem in numerous industrial applications. In recent years, deep learning enabled anomaly detection has emerged as a critical direction, however the improved detection accuracy and reconstruction is achieved with the utilization of large neural networks with more layers and nodes, increasing their storage and computational cost. Moreover, the data collected in edge devices contain user privacy, introducing challenges that can be successfully addressed by the very recent on-device privacy-preserving distributed machine learning paradigm, known as federated learning (FL). This paradigm allows edge devices to locally train and exchange models increasing also the communication cost. Thus, in order to deal with the increased communication, processing and storage challenges introduced by FL based deep anomaly detection NN pruning is expected to have significant benefits towards both reducing the processing, storage and communication complexity but also towards avoiding the over fitting problem. With this focus, a novel compression-based optimization problem is proposed at the server-side of a FL paradigm that simultaneously fusses the local models broadcast by the edge devices and performs pruning generating a much more compressed model to be deployed at the edge devices with no accuracy loss even if the training is performed using a small subset of data instances, achieving compression rates greater than 99%.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Towards Integrating Emerging AI Applications in SE Education

Towards Integrating Emerging AI Applications in SE Education

引用

Conference on Software engineering Education & Training (CSEE&T)

作者： Michael Vierhauser Iris Groher Tobias Antensteiner Clemens Sauerwein Department of Computer Science University of Innsbruck Innsbruck Austria Institute of Business Informatics - Software Engineering Johannes Kepler University Linz Linz Austria

ISBN: (数字)9798350378979

ISBN: (纸本)9798350378986

Artificial Intelligence (AI) approaches have been incorporated into modern learning environments and software engineering (SE) courses and curricula for several years. However, with the significant rise in popularity of large language models (LLMs) in general, and OpenAI's LLM-powered chatbot ChatGPT in particular in the last year, educators are faced with rapidly changing classroom environments and disrupted teaching principles. Examples range from programming assignment solutions that are fully generated via ChatGPT, to various forms of cheating during exams. However, despite these negative aspects and emerging challenges, AI tools in general, and LLM applications in particular, can also provide significant opportunities in a wide variety of SE courses, supporting both students and educators in meaningful ways. In this early research paper, we present preliminary results of a systematic analysis of current trends in the area of AI, and how they can be integrated into university-level SE curricula, guidelines, and approaches to support both instructors and learners. We collected both teaching and research papers and analyzed their potential usage in SE education, using the ACM computer Science Curriculum Guidelines CS2023. As an initial outcome, we discuss a series of opportunities for AI applications and further research areas.

关键词： Systematics Large language models Learning (artificial intelligence) Chatbots Market research Artificial intelligence engineering education

来源：评论

学校读者我要写书评

暂无评论

Awake at the Wheel: Enhancing Automotive Safety through EEG-Based Fatigue Detection

arXiv

引用

arXiv 2024年

作者： Siddhad, Gourav Dey, Sayantan Roy, Partha Pratim Iwamura, Masakazu Department of Computer Science and Engineering Indian Institute of Technology Uttarakhand Roorkee247667 India Department of Core Informatics Graduate School of Informatics Osaka Metropolitan University Sakai Osaka599-8531 Japan

Driver fatigue detection is increasingly recognized as critical for enhancing road safety. This study introduces a method for detecting driver fatigue using the SEED-VIG dataset, a well-established benchmark in EEG-based vigilance analysis. By employing advanced pattern recognition technologies, including machine learning and deep neural networks, EEG signals are meticulously analyzed to discern patterns indicative of fatigue. This methodology combines feature extraction with a classification framework to improve the accuracy of fatigue detection. The proposed NLMDA-Net reached an impressive accuracy of 83.71% in detecting fatigue from EEG signals by incorporating two novel attention modules designed specifically for EEG signals, the channel and depth attention modules. NLMDA-Net effectively integrate features from multiple dimensions, resulting in improved classification performance. This success stems from integrating temporal convolutions and attention mechanisms, which effectively interpret EEG data. Designed to capture both temporal and spatial characteristics of EEG signals, deep learning classifiers have proven superior to traditional methods. The results of this study reveal a substantial enhancement in detection rates over existing models, highlighting the efficacy of the proposed approach for practical applications. The implications of this research are profound, extending beyond academic realms to inform the development of more sophisticated driver assistance systems. Incorporating this fatigue detection algorithm into these systems could significantly reduce fatigue-related incidents on the road, thus fostering safer driving conditions. This paper provides an exhaustive analysis of the dataset, methods employed, results obtained, and the potential real-world applications of the findings, aiming to contribute significantly to advancements in automotive safety. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

FEDERATED INVERSE PROBABILITY TREATMENT WEIGHTING FOR INDIVIDUAL TREATMENT EFFECT ESTIMATION

arXiv

引用

arXiv 2025年

作者： Yin, Changchang Chen, Hong-You Chao, Wei-Lun Zhang, Ping Department of Computer Science and Engineering The Ohio State University ColumbusOH United States Department of Computer Science and Engineering Department of Biomedical Informatics Translational Data Analytics Institute The Ohio State University ColumbusOH United States

Individual treatment effect (ITE) estimation is to evaluate the causal effects of treatment strategies on some important outcomes, which is a crucial problem in healthcare. Most existing ITE estimation methods are designed for centralized settings. However, in real-world clinical scenarios, the raw data are usually not shareable among hospitals due to the potential privacy and security risks, which makes the methods not applicable. In this work, we study the ITE estimation task in a federated setting, which allows us to harness the decentralized data from multiple hospitals. Due to the unavoidable confounding bias in the collected data, a model directly learned from it would be inaccurate. One well-known solution is Inverse Probability Treatment Weighting (IPTW), which uses the conditional probability of treatment given the covariates to re-weight each training example. Applying IPTW in a federated setting, however, is non-trivial. We found that even with a well-estimated conditional probability, the local model training step using each hospital’s data alone would still suffer from confounding bias. To address this, we propose FED-IPTW, a novel algorithm to extend IPTW into a federated setting that enforces both global (over all the data) and local (within each hospital) decorrelation between covariates and treatments. We validated our approach on the task of comparing the treatment effects of mechanical ventilation on improving survival probability for patients with breadth difficulties in the intensive care unit (ICU). We conducted experiments on both synthetic and real-world eICU datasets and the results show that FED-IPTW outperform state-of-the-art methods on all the metrics on factual prediction and ITE estimation tasks, paving the way for personalized treatment strategy design in mechanical ventilation usage. © 2025, CC BY-NC-SA.

关键词： Inverse problems

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：