检索结果-内蒙古大学图书馆

Proceedings of the 2023 ACM/IEEE Joint Conference on Digital Libraries

作者： Don Brower David Butcher Angela Murillo Center for Research Computing University of Notre Dame Notre Dame Indiana USA CIMAR ICR National High Magnetic Field Laboratory Tallahassee Florida USA School of Informatics and Computing Indiana University-Purdue University Indianapolis Indianapolis Indiana USA

ISBN: (纸本)9798350399318

This workshop will bring together data managers, repository managers, administrators, and others who are responsible for, or interested in research data management at large research facilities. These facilities have unique issues due to a variety of factors, such as an extreme data volume, variety, and velocity. The workshop aims to provide cross-pollination between facilities that have similar desires to realize the FAIR principles. The organizers of this workshop are members of the NSF CI Compass FAIR Data Working Group, and the outcomes from these discussions will become a white paper and topics for future CI Compass webinars.

关键词： digital libraries

来源：评论

学校读者我要写书评

暂无评论

ASH: Animatable Gaussian Splats for Efficient and Photoreal Human Rendering

arXiv

引用

arXiv 2023年

作者： Pang, Haokai Zhu, Heming Kortylewski, Adam Theobalt, Christian Habermann, Marc Max Planck Institute for Informatics Saarland Informatics Campus Germany ETH Zürich Switzerland Universität Freiburg Germany Saarbrücken Research Center for Visual Computing Interaction and AI Germany

Real-time rendering of photorealistic and controllable human avatars stands as a cornerstone in Computer Vision and Graphics. While recent advances in neural implicit rendering have unlocked unprecedented photorealism for digital avatars, real-time performance has mostly been demonstrated for static scenes only. To address this, we propose ASH, an animatable Gaussian splatting approach for photorealistic rendering of dynamic humans in real time. We parameterize the clothed human as animatable 3D Gaussians, which can be efficiently splatted into image space to generate the final rendering. However, naively learning the Gaussian parameters in 3D space poses a severe challenge in terms of compute. Instead, we attach the Gaussians onto a deformable character model, and learn their parameters in 2D texture space, which allows leveraging efficient 2D convolutional architectures that easily scale with the required number of Gaussians. We benchmark ASH with competing methods on pose-controllable avatars, demonstrating that our method outperforms existing real-time methods by a large margin and shows comparable or even better results than offline methods. © 2023, CC BY.

关键词： Textures

来源：评论

学校读者我要写书评

暂无评论

Breast Cancer Prediction Using Classical Machine Learning Models

Breast Cancer Prediction Using Classical Machine Learning Mo...

引用

IEEE International Conference of Intelligent Robotic and Control Engineering (IRCE)

作者： Khawla Almazrouei Ali Bou Nassif Autonomous robotics research center UAE Technology innovation institute Abu Dhabi UAE College of Computing and Informatics University of Sharjah Sharjah UAE

Machine Learning is an emerging topic of study nowadays. Due to their extensive capacity for data exploration and the discovery of illuminating patterns, machine learning, and data mining techniques have become extremely popular. Data mining techniques are also used in the healthcare industry and have proven particularly effective for cancer prognosis and prediction. Breast cancer is the second most significant factor in women's cancer-related death rates. In that situation, early detection of benign and malignant stages of breast cancer can enable the patient, and the doctor administers the appropriate treatments. In this study, a breast cancer dataset of 569 instances and 32 features were chosen to predict the stages of breast cancer. Four classification algorithms are employed: Support Vector Machine, K Nearest Neighbor, Multi layer Perceptron, and Radial Basis Neural Network. The algorithm's performance regarding Accuracy, Precision, Recall, and Roc Area are compared, and it is found that SVM outperformed the others with an accuracy rate of 98.05%. The paper also demonstrated how feature selection strategies might shorten training times before proposing a reliable and universal model for predicting the breast cancer stage.

关键词：

来源：评论

学校读者我要写书评

暂无评论

How Good is ChatGPT in Giving Adaptive Guidance Using Knowledge Graphs in E-Learning Environments?

arXiv

引用

arXiv 2024年

作者： Ocheja, Patrick Flanagan, Brendan Dai, Yiling Ogata, Hiroaki University of Nigeria Nsukka Nigeria Graduate School of Information Science and Electrical Engineering Kyushu University Japan Academic Center for Computing and Media Studies Kyoto University Japan Learning and Educational Technologies Research Unit Academic Center for Computing and Media Studies Graduate School of Informatics at Kyoto University Japan

E-learning environments are increasingly harnessing large language models (LLMs) like GPT-3.5 and GPT-4 for tailored educational support. This study introduces an approach that integrates dynamic knowledge graphs with LLMs to offer nuanced student assistance. By evaluating past and ongoing student interactions, the system identifies and appends the most salient learning context to prompts directed at the LLM. Central to this method is the knowledge graph's role in assessing a student's comprehension of topic prerequisites. Depending on the categorized understanding (good, average, or poor), the LLM adjusts its guidance, offering advanced assistance, foundational reviews, or in-depth prerequisite explanations, respectively. Preliminary findings suggest students could benefit from this tiered support, achieving enhanced comprehension and improved task outcomes. However, several issues related to potential errors arising from LLMs were identified, which can potentially mislead students. This highlights the need for human intervention to mitigate these risks. This research aims to advance AI-driven personalized learning while acknowledging the limitations and potential pitfalls, thus guiding future research in technology and data-driven education. © 2024, CC BY.

关键词： Knowledge graph

来源：评论

学校读者我要写书评

暂无评论

Answering Why? An Overview of Immersive Data Visualization Applications Using Multi-Level Typology of Visualization Task

Answering Why? An Overview of Immersive Data Visualization A...

引用

2022 IEEE International Conference on computing, ICOCO 2022

作者： Jamaludin, Najwa Ayuni Binti Mohamed, Farhan Bin Sunar, Mohd Shahrizal Bin Selamat, Ali Bin Krejcar, Ondrej School of Computing Faculty of Engineering Johor Bahru Malaysia Johor Bahru Malaysia Kuala Lumpur Malaysia University of Hradec Kralove Center for Basic and Applied Research Faculty of Informatics and Management Hradec Kralove Czech Republic

ISBN: (纸本)9781665489966

Immersive Analytics (IA) is a fast-growing research field that concerns improving and facilitating human sensemaking and data understanding through an immersive experience. Understanding the suitable application scenario that will benefit from IA enables a shift towards developing effective and meaningful applications. This paper aims to explore tasks and scenarios that can benefit from IA by conducting a systematic review of existing studies and mapping them according to the multi-level typology for abstract visualization tasks, which is also known as the What-Why-How framework. The study synthesized several works to answer the Why within the context of multiple levels of specificity. Finally, the limitations and future works are discussed. © 2022 IEEE.

关键词： Data visualization

来源：评论

学校读者我要写书评

暂无评论

Machine and Deep Learning Dominate Recent Innovations in Sensors, Signals and Imaging informatics

引用

Yearbook of Medical informatics 2023年第1期32卷 282-285页

作者： Baumgartner, Christian Rittner, Leticia Deserno, Thomas M. Institute of Health Care Engineering with European Testing Center of Medical Devices Graz University of Technology Austria Medical Imaging Computing Lab (MICLab) School of Electrical and Computer Engineering University of Campinas Brazil Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School Braunschweig Germany

OBJECTIVES: This review presents research papers highlighting notable developments and trends in sensors, signals, and imaging informatics (SSII) in 2022. METHOD: We performed a bibliographic search in PubMed combining Medical Subject Heading (MeSH) terms and keywords to create particular queries for sensors, signals, and imaging informatics. Only papers published in journals containing greater than three articles in the search query were considered. Using a three-point Likert scale (1 = not include, 2 = perhaps include, 3 = include), we reviewed the titles and abstracts of all database results. Only articles that scored three times Likert scale 3, or two times Likert scale 3, and one time Likert scale 2 were considered for full paper review. On this pre-selection, only papers with a total of at least eight points of the three section co-editors were considered for external review. Based on the external reviewers, we selected the top two papers representing significant research in SSII. RESULTS: Among the 469 returned papers published in 2022 in the various areas of SSII, 90, 31, and 348 papers for sensors, signals, and imaging informatics, and then, the full review process selected the two best papers. From the 469 papers, the section co-editors identified 29 candidate papers with at least 8 Likert points in total, of which 9 were nominated as the best contributions after a full paper assessment. Five external reviewers evaluated the nominated papers, and the two highest-scoring papers were selected based on the overall scores of all external reviewers. A consensus of the International Medical informatics Association (IMIA) Yearbook editorial board finally approved the nominated papers. Machine and deep learning-based techniques continue to be the dominant theme in this field. CONCLUSIONS: Sensors, signals, and imaging informatics is a dynamic field of intensive research with increasing practical applications to support medical decision-making on a personalized bas

关键词： Biomedical informatics deep learning machine learning personalized medicine

来源：评论

学校读者我要写书评

暂无评论

Online Learning to Cache and Recommend in the Next Generation Cellular Networks

IEEE Transactions on Machine Learning in Communications and ...

引用

IEEE Transactions on Machine Learning in Communications and Networking 2024年 2卷 511-525页

作者： Tharakan, Krishnendu S Bharath, B.N. Bhatia, Vimal Queen's University School of Computing KingstonONK7L 3N6 Canada Dharwad580011 India Indian Institute of Technology Indore Department of Electrical Engineering Center for Advanced Electronics Indore453552 India University of Hradec Králové Faculty of Informatics and Management Hradec Králové500 03 Czech Republic Soochow University School of Electronic and Information Engineering Suzhou215006 China

An efficient caching can be achieved by predicting the popularity of the files accurately. It is well known that the popularity of a file can be nudged by using recommendation, and hence it can be estimated accurately leading to an efficient caching strategy. Motivated by this, in this paper, we consider the problem of joint caching and recommendation in a 5G and beyond heterogeneous network. We model the influence of recommendation on demands by a Probability Transition Matrix (PTM). The proposed framework consists of estimating the PTM and use them to jointly recommend and cache the files. In particular, this paper considers two estimation methods namely a) Bayesian estimation and b) a genie aided Point estimation. An approximate high probability bound on the regret of both the estimation methods are provided. Using this result, we show that the approximate regret achieved by the genie aided Point estimation approach is mathcal {O}(T{2/3} sqrt {log T}) while the Bayesian estimation method achieves a much better scaling of mathcal {O}(sqrt {T}). These results are extended to a heterogeneous network consisting of M small base stations (SBSs) with a central macro base station. The estimates are available at multiple SBSs, and are combined using appropriate weights. Insights on the choice of these weights are provided by using the derived approximate regret bound in the multiple SBS case. Finally, simulation results confirm the superiority of the proposed algorithms in terms of average cache hit rate, delay and throughput. © 2023 CCBY.

关键词： Heterogeneous networks

来源：评论

学校读者我要写书评

暂无评论

SODIBOT: A TWO in ONE Real-Time Human Monitoring System using YOLO algorithms

SODIBOT: A TWO in ONE Real-Time Human Monitoring System usin...

引用

International Conference on Software Engineering and Computer Systems (ICSECS)

作者： Azim Zaliha Abd Aziz Nurul Nadzirah Adnan Nur Farraliza Mansor Wan Suryani Wan Awang Ida Nurhaida Safitri Jaya Faculty of Informatics & Computing Universiti Sultan Zainal Abidin Terengganu Malaysia Department of Informatics Universitas Pembangunan Jaya Tangerang Selatan Indonesia Center for Urban Studies Universitas Pembangunan Jaya Tangerang Selatan Indonesia

COVID-19 primarily spreads through direct physical contact. As a precaution, it is recommended that each individual to keep at distance of at least one meter between one another. This study proposes a realtime system named as Social Distancing and Body Temperature (SODIBOT) for monitoring physical distancing compliance and body temperature measurement in indoor spaces during this endemic using computer vision and deep learning techniques. The method suggested in the study utilizes the You Only Look Once (YOLO) object detection algorithm to detect individuals and promptly estimate their physical distance in realtime using a high-end thermal camera. Every identified human is assigned a color-coded bounding box with a distinct meaning. Simultaneously, body temperature is also recorded and displayed at the top of each bounding box. The effectiveness of the proposed system was measured by the number of people detected in frame per second. Furthermore, the system's ability to measure and display individual body temperatures at the top of the bounding boxes adds additional value to SODIBOT. The outcomes illustrate the potential of SODIBOT in effectively monitoring compliance with physical distancing in indoor environments, offering valuable insights for potential application in other public health scenarios.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Beyond Self-Consistency: Ensemble Reasoning Boosts Consistency and Accuracy of LLMs in Cancer Staging

arXiv

引用

arXiv 2024年

作者： Chang, Chia-Hsuan Lucas, Mary M. Lee, Yeawon Yang, Christopher C. Lu-Yao, Grace College of Computing & Informatics Drexel University PhiladelphiaPA United States Department of Medical Oncology Sidney Kimmel Cancer Center Thomas Jefferson University PhiladelphiaPA United States

Advances in large language models (LLMs) have encouraged their adoption in the healthcare domain where vital clinical information is often contained in unstructured notes. Cancer staging status is available in clinical reports, but it requires natural language processing to extract the status from the unstructured text. With the advance in clinical-oriented LLMs, it is promising to extract such status without extensive efforts in training the algorithms. Prompting approaches of the pre-trained LLMs that elicit a model's reasoning process, such as chainof- thought, may help to improve the trustworthiness of the generated responses. Using self-consistency further improves model performance, but often results in inconsistent generations across the multiple reasoning paths. In this study, we propose an ensemble reasoning approach with the aim of improving the consistency of the model generations. Using an open access clinical large language model to determine the pathologic cancer stage from real-world pathology reports, we show that the ensemble reasoning approach is able to improve both the consistency and performance of the LLM in determining cancer stage, thereby demonstrating the potential to use these models in clinical or other domains where reliability and trustworthiness are critical. © 2024, CC BY.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

Vision-Based PM2.5 Concentration Estimation with Natural Scene Statistical Analysis

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2805-2815页

作者： Wang, Guangcheng Shi, Quan Wang, Han Gu, Ke Wei, Mengting Wong, Lai Kuan Wang, Mingxing Nantong University School of Transportation and Civil Engineering Nantong226019 China Beijing University of Technology Faculty of Information Technology Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education Beijing Laboratory of Smart Environmental Protection Beijing Artificial Intelligence Institute Beijing100124 China Chinese Academy of Sciences Institute of Software Beijing100045 China Multimedia University Faculty of Computing and Informatics Cyberjaya63100 Malaysia Environmental Science and Technology Company Ltd. Beijing102100 China

As the primary pollutant in China's urban atmosphere, particulate matter (PM)2.5 poses a great threat to the health of residents and ecological stability. An efficient and effective PM2.5 concentration monitoring is essential. Nonetheless, the popular devices for PM2.5 monitoring are developed based on two standards: the microoscillation balance method and the β-ray method, which have high purchase and maintenance costs and slow calculation rates. To this end, we put forward a real-time and reliable vision-based estimation algorithm of PM2.5 concentration. To be specific, the proposed method first develops two natural scene statistical analysis-based visual priors to measure saturation and structural information losses caused by the 'haze' formed by PM2.5. Moreover, we develop a lightweight deep belief network-deep neural network-based PM2.5 concentration estimation model, which learns the mapping from the designed visual priors to PM2.5 concentrations. Experiments confirm the superiority of our vision-based PM2.5 concentration estimation method by comparison with state-of-the-art photo-based PM2.5 monitoring methods. © 2020 IEEE.

关键词： Entropy

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：