检索结果-内蒙古大学图书馆

Towards Predictable Process and Consequence Attributes of data-Driven Group Work: Primary Analysis for Assisting Teachers with Automatic Group Formation

引用

Educational Technology and Society 2023年第4期26卷 90-103页

作者： Liang, Changhao Horikoshi, Izumi Majumdar, Rwitajit Flanagan, Brendan Ogata, Hiroaki Graduate School of Informatics Kyoto University Japan Academic Center for Computing and Media Studies Kyoto University Japan Center for Innovative Research and Education in Data Science Kyoto University Japan

data-driven platforms with rich data and learning analytics applications provide immense opportunities to support collaborative learning such as algorithmic group formation systems based on learning logs. However, teachers can still get overwhelmed since they have to manually set the parameters to create groups and it takes time to understand the meaning of each indicator. Therefore, it is imperative to explore predictive indicators for algorithmic group formation to release teachers from the dilemma with explainable group formation indicators and recommended settings based on group work purposes. Employing learning logs of group work from a reading-based university course, this study examines how learner indicators from different dimensions before the group work connect to the subsequent group work processes and consequences attributes through correlation analysis. Results find that the reading engagement and previous peer ratings can reveal individual achievement of the group work, and a homogeneous grouping strategy based on reading annotations and previous group work experience can predict desirable group performance for this learning context. In addition, it also proposes the potential of automatic group formation with recommended parameter settings that leverage the results of predictive indicators. © (2023). All Rights Reserved.

关键词： Correlation analysis CSCL GLOBE Group formation Group work indicator Group work prediction Teacher assistance

来源：评论

学校读者我要写书评

暂无评论

A Communication-Aware and Energy-Efficient Genetic Programming Based Method for Dynamic Resource Allocation in Clouds 28th

A Communication-Aware and Energy-Efficient Genetic Programm...

引用

28th European Conference on Applications of Evolutionary Computation, EvoApplications 2025, held as part of EvoStar 2025

作者： Fang, Zhengxin Ma, Hui Chen, Gang Hartmann, Sven Chen, Shiping Centre for Data Science and Artificial Intelligence and School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand Department of Informatics Clausthal University of Technology Clausthal-Zellerfeld Germany CSIRO Data61 Sydney Australia

ISBN: (纸本)9783031900648

Loosely coupled microservices have emerged as a new paradigm for efficiently deploying applications in clouds. However, dynamic resource allocation in clouds introduces significant challenges to microservice application deployment. On the one hand, frequent invocations between microservices may lead to substantial communication overhead if microservices are not allocated properly. On the other hand, the increasing number of microservices in modern applications makes it very challenging to minimize the energy consumption of a cloud data center as it introduces a bi-level optimization problem with an extremely large search space. In this paper, we propose a new communication-aware and energy-efficient genetic programming based method that automatically learns heuristics for dynamic resource allocation to jointly minimize the communication overhead and the energy consumption. Comprehensive experiments using real-world datasets show that our proposed method can evolve effective heuristics that noticeably outperform existing approaches for dynamic microservice deployment in clouds. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Genetic programming

来源：评论

学校读者我要写书评

暂无评论

Artificial intelligence in physiological characteristics recognition for internet of things authentication

引用

Digital Communications and Networks 2024年第3期10卷 740-755页

作者： Zhimin Zhang Huansheng Ning Fadi Farha Jianguo Ding Kim-Kwang Raymond Choo School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing100083China Faculty of Informatics Engineering Aleppo UniversityAleppoSyria Department of Computer Science Blekinge Institute of Technology37179KarlskronaSweden Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Effective user authentication is key to ensuring equipment security,data privacy,and personalized services in Internet of Things(IoT)***,conventional mode-based authentication methods(e.g.,passwords and smart cards)may be vulnerable to a broad range of attacks(e.g.,eavesdropping and side-channel attacks).Hence,there have been attempts to design biometric-based authentication solutions,which rely on physiological and behavioral *** characteristics need continuous monitoring and specific environmental settings,which can be challenging to implement in ***,we can also leverage Artificial Intelligence(AI)in the extraction and classification of physiological characteristics from IoT devices processing to facilitate ***,we review the literature on the use of AI in physiological characteristics recognition pub-lished after *** use the three-layer architecture of the IoT(i.e.,sensing layer,feature layer,and algorithm layer)to guide the discussion of existing approaches and their *** also identify a number of future research opportunities,which will hopefully guide the design of next generation solutions.

关键词： Physiological characteristics recognition Artificial intelligence Internet of things Biological-driven authentication

来源：评论

学校读者我要写书评

暂无评论

Health Information Exchange: Contributions from 2023

引用

Yearbook of Medical informatics 2025年第1期33卷 191-194页

作者： Bloomrosen, Meryl Feldman, Sue S. American Medical Association Chicago IL United States Graduate Programs in Health Informatics Department of Health Services Administration University of Alabama at Birmingham Birmingham AL United States Department of Biomedical Informatics and Data Science Heersink School of Medicine University of Alabama at Birmingham Birmingham AL United States

OBJECTIVES: To summarize the recent literature and research and present a selection of the best papers published online and in print in 2023 related to health information exchange (HIE). METHODS: Using Covidence as a screening and analysis tool, a systematic review of the literature was independently conducted by the two section editors. Seven studies emerged as suitable for final IMIA Yearbook consideration. RESULTS: Among the papers reviewed, three major themes emerged: clinical services utilization, continuity of care, and public and population health. These themes represent an increased breadth and depth of HIE application. CONCLUSIONS: Review of the literature suggested more studies with the use of data from HIEs, perhaps suggesting increased trust in data accuracy, adequacy, and completeness. The section editors noted the increase in papers from diverse countries describing applications of HIEs suggesting more widespread implementation of HIEs worldwide. As health data standards are developed and adopted globally, this could set the stage for increased international health data exchange. The larger corpus of 2023 literature reviewed resulted in conversations by the section editors on the changing landscape of the expanding, maturing, and innovative use cases for HIEs and HIE data. This landscape bears continued watching in 2024. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://***/licenses/by/4.0/).

关键词： data Sharing Electronic Health Records Health Information Health Information Exchange Health Information Technology International Medical informatics Association Yearbook Interoperability

来源：评论

学校读者我要写书评

暂无评论

Reducing Uncertainty in 3D Medical Image Segmentation under Limited Annotations through Contrastive Learning 7

Reducing Uncertainty in 3D Medical Image Segmentation under ...

引用

7th International Conference on Medical Imaging with Deep Learning, MIDL 2024

作者： Karimijafarbigloo, Sanaz Azad, Reza Kazerouni, Amirhossein Merhof, Dorit Faculty of Informatics and Data Science University of Regensburg Regensburg Germany RWTH Aachen University Aachen Germany School of Electrical Engineering Iran University of Science and Technology Tehran Iran Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany

Despite recent successes in semi-supervised learning for natural image segmentation, applying these methods to medical images presents challenges in obtaining discriminative representations from limited annotations. While contrastive learning frameworks excel in similarity measures for classification, their transferability to precise pixel-level segmentation in medical images is hindered, particularly when confronted with inherent prediction uncertainty. To overcome this issue, our approach incorporates two subnetworks to rectify erroneous predictions. The first network identifies uncertain predictions, generating an uncertainty attention map. The second network employs an uncertainty-aware descriptor to refine the representation of uncertain regions, enhancing the accuracy of predictions. Additionally, to adaptively recalibrate the representation of uncertain candidates, we define class prototypes based on reliable predictions. We then aim to minimize the discrepancy between class prototypes and uncertain predictions through a deep contrastive learning strategy. Our experimental results on organ segmentation from clinical MRI and CT scans demonstrate the effectiveness of our approach compared to state-of-the-art methods. Code. © 2024 CC-BY 4.0, S.K., R.A., A.K. & D.M.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Leveraging LLM in Genetic Programming Hyper-heuristics for Dynamic Microservice Deployment 37th

Leveraging LLM in Genetic Programming Hyper-heuristics for...

引用

37th Australasian Joint Conference on Artificial Intelligence, AJCAI 2024

作者： Fang, Zhengxin Ma, Hui Chen, Gang Hartmann, Sven Wang, Chen Centre for Data Science and Artificial Intelligence and School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand Department of Informatics Clausthal University of Technology Clausthal-Zellerfeld Germany National Institute of Water and Atmospheric Research Wellington New Zealand

ISBN: (纸本)9789819603473

Microservice deployment in cloud computing is a challenging combinatorial optimization problem due to the complex dependencies among microservices and the intricate trade-offs among different QoS requirements, e.g., minimizing Energy Consumption (EC) vs. minimizing Communication Overhead (CO). Recently, some hyper-heuristics methods, particularly Genetic Programming Hyper-Heuristics (GPHH), have been proposed to automatically generate heuristics for solving dynamic microservice deployment problems. Meanwhile, Large Language Models (LLMs) are becoming popular for solving various domain-specific problems thanks to their strong ability to learn problem-related knowledge. However, hybridizing GPHH with LLM by combining their abilities in solving complex optimization problems remains unexplored. In this paper, we propose an LLM-enhanced Genetic Programming Hyper-Heuristic (LLM-GPHH) algorithm to evolve heuristics for the dynamic deployment of applications composed of microservices, to jointly optimize EC and CO. Our experiments on real-world datasets demonstrate the effectiveness of the newly proposed LLM-GPHH. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Genetic programming

来源：评论

学校读者我要写书评

暂无评论

An Optimised BERT Pretraining Approach for Identification of Targeted Offensive Language: data Imbalance and Potential Solutions 4

An Optimised BERT Pretraining Approach for Identification of...

引用

4th International Conference on Computing and Communication Systems, I3CS 2023

作者： Mifsud, Ruth Deka, Lipika Lahiri, Indrani Data Reply London United Kingdom De Montfort University School of Computer Science and Informatics Leicester United Kingdom De Montfort University Leicester Media School Leicester United Kingdom

ISBN: (纸本)9798350323771

Targeted offensive comments and hate speech on online media platforms are on the rise, with evidential mental health consequences including suicide. Several NLP techniques have been proposed and in use. However, data imbalance in the training dataset is stopping them from performing at full potential. Solutions include under-sampling of the majority class, oversampling of the minority class or introducing synthetic samples. These approaches present with their own unique problems - that of critical information loss, overfitting and non-generalised models. The presented research explores these approaches for addressing the data imbalance problem, by varying the under/over/synthetic sampling rate and studying the performance as well as the generalisability of the models. © 2023 IEEE.

关键词： Natural language processing systems

来源：评论

学校读者我要写书评

暂无评论

An Evaluation of Pretrained BERT Models for Comparing Semantic Similarity Across Unstructured Clinical Trial Texts

An Evaluation of Pretrained BERT Models for Comparing Semant...

引用

作者： Patricoski, Jessica Kreimeyer, Kory Balan, Archana Hardart, Kent Tao, Jessica Anagnostou, Valsamo Botsis, Taxiarchis Biomedical Informatics and Data Science Section Johns Hopkins University School of Medicine BaltimoreMD United States

ISBN: (纸本)9781643682501

Processing unstructured clinical texts is often necessary to support certain tasks in biomedicine, such as matching patients to clinical trials. Among other methods, domain-specific language models have been built to utilize free-text information. This study evaluated the performance of Bidirectional Encoder Representations from Transformers (BERT) models in assessing the similarity between clinical trial texts. We compared an unstructured aggregated summary of clinical trials reviewed at the Johns Hopkins Molecular Tumor Board with the *** records, focusing on the titles and eligibility criteria. Seven pretrained BERT-Based models were used in our analysis. Of the six biomedical-domain-specific models, only SciBERT outperformed the original BERT model by accurately assigning higher similarity scores to matched than mismatched trials. This finding is promising and shows that BERT and, likely, other language models may support patient-trial matching. © 2022 The authors and IOS Press.

关键词： Medical applications

来源：评论

学校读者我要写书评

暂无评论

MatrikalinDiabetes: User-Centered Design of a mHealth App for Gestational Diabetes Mellitus Management and Education Among Bangladeshi Women 26th

MatrikalinDiabetes: User-Centered Design of a mHealth App ...

引用

26th International Conference on Human-Computer Interaction, HCII 2024

作者： Ratul, Mohammad Arshad Hossain Yanoor Bristy, Tunisha Sayeed, Noorjahan Islam, Ashraful Chaudhry, Beenish Moalla Center for Computational and Data Sciences Independent University Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Independent University Bangladesh Dhaka Bangladesh School of Computing and Informatics University of Louisiana at Lafayette Louisiana United States

ISBN: (纸本)9783031768088

This study employs the User-Centered Design (UCD) methodology to develop a mobile health (mHealth) application (app) specifically tailored for Bangladeshi women with Gestational Diabetes Mellitus (GDM). GDM affects approximately 10% of pregnant women globally and around 35% within Bangladesh, as reported by the World Health Organization (WHO). Despite its high prevalence, there is a significant lack of awareness and education about GDM in Bangladesh, compounded by language barriers that make existing digital solutions less accessible. The MatrikalinDiabetes mHealth app aims to overcome these barriers by providing comprehensive management and educational resources for GDM in the Bangla language, addressing both pre-pregnancy and post-pregnancy needs. To ensure the app meets the actual needs of potential users, personas were created based on a literature review, and a survey was conducted. This process informed the development of both a low-fidelity, paper-based prototype and a high-fidelity digital prototype. Key features of the MatrikalinDiabetes app include tracking of food and water intake, physical activity monitoring, reminders, GDM education, chat forums for community support, blood glucose level (BGL) monitoring, an SOS button for emergency contacts, and quizzes for user engagement. Preliminary feedback indicated that 53.8% of participants favored the integrated features and expressed willingness to use and recommend the app. The survey of the high-fidelity prototype yielded positive responses, with 57.9% of participants strongly agreeing and 42.1% agreeing that the features were beneficial. The user-friendly design of the high-fidelity prototype ensures intuitive interaction, aiding in health management, enhancing GDM understanding, and promoting a healthier lifestyle among pregnant women in Bangladesh. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Obstetrics

来源：评论

学校读者我要写书评

暂无评论

Local and Global Feature-Aware Dual-Branch Networks for Plant Disease Recognition

引用

植物表型组学（英文） 2024年第4期6卷 828-840页

作者： Jianwu Lin Xin Zhang Yongbin Qin Shengxian Yang Xingtian Wen Tomislav Cernava Quirico Migheli Xiaoyulong Chen Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China School of Biological Sciences Faculty of Environmental and Life SciencesUniversity of SouthamptonSouthampton S017 1BJUK Dipartimento di Agraria and NRD-Nucleo di Ricerca sulla Desertificazione Università degli Studi di SassariSassariItaly Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Life Sciences Guizhou UniversityGuiyang 550025China

Accurate identification of plant diseases is important for ensuring the safety of agricultural *** neural networks(CNNs)and visual transformers(VTs)can extract effective representations of images and have been widely used for the intelligent recognition of plant disease ***,CNNs have excellent local perception with poor global perception,and VTs have excellent global perception with poor local *** makes it difficult to further improve the performance of both CNNs and VTs on plant disease recognition *** this paper,we propose a local and global feature-aware dual-branch network,named LGNet,for the identification of plant *** specifically,we first design a dual-branch structure based on CNNs and VTs to extract the local and global ***,an adaptive feature fusion(AFF)module is designed to fuse the local and global features,thus driving the model to dynamically perceive the weights of different ***,we design a hierarchical mixed-scale unit-guided feature fusion(HMUFF)module to mine the key information in the features at different levels and fuse the differentiated information among them,thereby enhancing the model's multiscale perception ***,extensive experiments were conducted on the Al Challenger 2018 dataset and the self-collected corn disease(SCD)*** experimental results demonstrate that our proposed LGNet achieves state-of-the-art recognition performance on both the Al Challenger 2018 dataset and the SCD dataset,with accuracies of 88.74％and 99.08％,respectively.

关键词： global plant disease dual-branch feature-aware local networks recognition

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：