检索结果-内蒙古大学图书馆

Classification of cow diet based on milk Mid Infrared Spectra: A data analysis competition at the "International Workshop on Spectroscopy and Chemometrics 2022"

Classification of cow diet based on milk Mid Infrared Spectr...

引用

作者： Frizzarin, Maria Visentin, Giulio Ferragina, Alessandro Hayes, Elena Bevilacqua, Antonio Dhariyal, Bhaskar Domijan, Katarina Khan, Hussain Ifrim, Georgiana Nguyen, Thach Le Meagher, Joe Menchetti, Laura Singh, Ashish Whoriskey, Suzy Williamson, Robert Zappaterra, Martina Casa, Alessandro Teagasc Animal & Grassland Research and Innovation Centre Moorepark Ireland School of Mathematics and Statistics University College Dublin Ireland Department of Veterinary Medical Sciences University of Bologna Italy Teagasc Food Research Centre Ashtown Ireland Teagasc Food Research Centre Moorepark Ireland School of Computer Science University College Dublin Ireland Department of Mathematics and Statistics National University of Ireland Maynooth Ireland Insight Centre for Data Analytics University College Dublin Ireland School of Biosciences and Veterinary Medicine University of Camerino Italy School of Electronics Electrical Engineering and Computer Science Queen's University Belfast United Kingdom Department of Agricultural and Food Sciences University of Bologna Italy Faculty of Economics and Management Free University of Bozen-Bolzano Italy

In April 2022, the Vistamilk SFI Research Centre organized the second edition of the "International Workshop on Spectroscopy and Chemometrics – Applications in Food and Agriculture". Within this event, a data challenge was organized among participants of the workshop. Such data competition aimed at developing a prediction model to discriminate dairy cows’ diet based on milk spectral information collected in the mid-infrared region. In fact, the development of an accurate and reliable discriminant model for dairy cows’ diet can provide important authentication tools for dairy processors to guarantee product origin for dairy food manufacturers from grass-fed animals. Different statistical and machine learning modelling approaches have been employed during the workshop, with different pre-processing steps involved and different degree of complexity. The present paper aims to describe the statistical methods adopted by participants to develop such classification model. © 2023

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Confounder-Aware Medical data Selection for Fine-Tuning Pretrained Vision Models

Confounder-Aware Medical Data Selection for Fine-Tuning Pret...

引用

IEEE International Symposium on Biomedical Imaging

作者： Anyang Ji Qingbo Kang Wei Xu Changfan Wang Kang Li Qicheng Lao West China Biomedical Big Data Center West China Hospital Sichuan University Chengdu China College of Computer Science Sichuan University Chengdu China Shanghai Artificial Intelligence Laboratory Shanghai China School of Biomedical Engineering University of Science and Technology of China Hefei China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

The emergence of large-scale pre-trained vision foundation models has greatly advanced the medical imaging field through the pre-training and fine-tuning paradigm. However, selecting appropriate medical data for downstream fine-tuning remains a significant challenge considering its annotation cost, privacy concerns, and the detrimental effects of confounding variables. In this work, we present a confounder-aware medical data selection approach for medical dataset curation aiming to select minimal representative data by strategically mitigating the undesirable impact of confounding variables while preserving the natural distribution of the dataset. Our approach first identifies confounding variables within data and then develops a distance-based data selection strategy for confounder-aware sampling with a constrained budget in the data size. We validate the superiority of our approach through extensive experiments across diverse medical imaging modalities, highlighting its effectiveness in addressing the substantial impact of confounding variables and enhancing the fine-tuning efficiency in the medical imaging domain, compared to other data selection approaches.

关键词： Visualization data privacy Costs Foundation models Annotations Clustering algorithms Manuals data models Calibration Biomedical imaging

来源：评论

学校读者我要写书评

暂无评论

Reward-agnostic Fine-tuning: Provable Statistical Benefits of Hybrid Reinforcement Learning

arXiv

引用

arXiv 2023年

作者： Li, Gen Zhan, Wenhao Lee, Jason D. Chi, Yuejie Chen, Yuxin Department of Statistics and Data Science Wharton School University of Pennsylvania PhiladelphiaPA19104 United States Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States Department of Electrical and Computer Engineering Carnegie Mellon University PittsburghPA15213 United States

This paper studies tabular reinforcement learning (RL) in the hybrid setting, which assumes access to both an offline dataset and online interactions with the unknown environment. A central question boils down to how to efficiently utilize online data collection to strengthen and complement the offline dataset and enable effective policy fine-tuning. Leveraging recent advances in reward-agnostic exploration and model-based offline RL, we design a three-stage hybrid RL algorithm that beats the best of both worlds — pure offline RL and pure online RL — in terms of sample complexities. The proposed algorithm does not require any reward information during data collection. Our theory is developed based on a new notion called single-policy partial concentrability, which captures the trade-off between distribution mismatch and miscoverage and guides the interplay between offline and online data. © 2023, CC0.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Graph Neural Networks for Wireless Networks: Graph Representation, Architecture and Evaluation

arXiv

引用

arXiv 2024年

作者： Lu, Yang Li, Yuhang Zhang, Ruichen Chen, Wei Ai, Bo Niyato, Dusit School of Computer and Technology China Collaborative Innovation Center of Railway Traffic Safety Beijing Jiaotong University Beijing100044 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China

Graph neural networks (GNNs) have been regarded as the basic model to facilitate deep learning (DL) to revolutionize resource allocation in wireless networks. GNN-based models are shown to be able to learn the structural information about graphs representing the wireless networks to adapt to the time-varying channel state information and dynamics of network topology. This article aims to provide a comprehensive overview of applying GNNs to optimize wireless networks via answering three fundamental questions, i.e., how to input the system parameters of wireless networks into GNNs, how to improve the expressive performance of GNNs, and how to evaluate GNNs. Particularly, two graph representations are given to transform wireless network parameters into graph-structured data. Then, we focus on the architecture design of the GNN-based models via introducing the basic message passing as well as model improvement methods including multi-head attention mechanism and residual structure. At last, we give task-oriented evaluation metrics for DL-enabled wireless resource allocation schemes. We also highlight certain challenges and potential research directions for the application of GNNs in wireless networks. © 2024, CC BY.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

An Exploratory Investigation of Log Anomalies in Unmanned Aerial Vehicles

An Exploratory Investigation of Log Anomalies in Unmanned Ae...

引用

International Conference on Software engineering (ICSE)

作者： Dinghua Wang Shuqing Li Guanping Xiao Yepang Liu Yulei Sui Pinjia He Michael R. Lyu University of Technology Sydney Australia Southern University of Science and Technology China The Chinese University of Hong Kong China Nanjing University of Aeronautics and Astronautics China Department of Computer Science and Engineering Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology China The University of New South Wales Australia School of Data Science The Chinese University of Hong Kong Shenzhen (CUHK-Shenzhen) China Shenzhen Research Institute of Big Data China

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

Unmanned aerial vehicles (UAVs) are becoming increasingly ubiqui-tous in our daily lives. However, like many other complex systems, UAVs are susceptible to software bugs that can lead to abnormal system behaviors and undesirable consequences. It is crucial to study such software bug-induced UAV anomalies, which are often mani-fested in flight logs, to help assure the quality and safety of UAV systems. However, there has been limited research on investigating the code-level patterns of software bug-induced UAV anomalies. This impedes the development of effective tools for diagnosing and localizing bugs within UAV system code. To bridge the research gap and deepen our understanding of UAV anomalies, we carried out an empirical study on this subject. We first collected 178 real-world abnormal logs induced by soft-ware bugs in two popular open-source UAV platforms, i.e., PX4 and Ardupilot. We then examined each of these abnormal logs and com-piled their common patterns. In particular, we investigated the most severe anomalies that led to UAV crashes, and identified their features. Based on our empirical findings, we further summarized the challenges of localizing bugs in system code by analyzing anoma-lous UAV flight data, which can offer insights for future research in this field.

关键词： Bridges Codes computer bugs Autonomous aerial vehicles Software Safety Complex systems

来源：评论

学校读者我要写书评

暂无评论

Predicting Hand Injury Severity Using Bayesian Networks in Cloud-Based Emergency Medicine

Predicting Hand Injury Severity Using Bayesian Networks in C...

引用

International Conference on Communications and Signal Processing

作者： S Yuvaraj K. Thinakaran S Porandla Srinivas S. Sivakumar M.V Ishwarya P. Thirumaraiselvan Department of Electronics and Communication Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Information Technology Malla Reddy Institute of Engineering and Technology Hyderabad Telangana India Department of Computer Science Thanthai Hans Roever College (Autonomous) Perambalur Tamil Nadu India Artificial Intelligence and Data science Department Agni College of Technology Thazhambur Tamil Nadu India Department of Electronics and Communication Engineering Adhiparasakthi Engineering College Melmaruvathur Tamil Nadu India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

Predicting the severity of hand injuries in emergency care is the focus of this study, which suggests using cloud-based Bayesian networks. It is crucial to have accurate severity prediction in order to treat emergency hand injuries promptly since they are prevalent. By using the scalability and flexibility of cloud computing, this method constructs a Bayesian severity assessment network with patient data, injury characteristics, and historical medical records. The model’s training data comes from a massive database of hand injury cases with different results. Emergency medical professionals may use real-time parameter modifications to strengthen and adapt the Bayesian network. Through the use of the cloud, healthcare providers may simply exchange data and reach consensus. It demonstrates that, compared to other well-established methods of evaluating hand injury severity, the model performs better in making predictions. The suggested method improves severity forecasts and advances cloud-based emergency care. The method for emergency hand injury care uses Bayesian networks in the cloud to improve scalability and accessibility. The findings show that the data-driven and efficient hand injury severity prediction method might transform emergency care by improving patient outcomes.

关键词： Cloud computing Adaptation models Scalability Training data Medical services Transforms Predictive models

来源：评论

学校读者我要写书评

暂无评论

Multi-task Learning for Multi-turn Dialogue Generation with Topic Drift Modeling 12

Multi-task Learning for Multi-turn Dialogue Generation with ...

引用

12th IEEE International Conference on Big Knowledge, ICBK 2021

作者： Zeng, Hongwei Hong, Zhenjie Liu, Jun Wei, Bifan Xi'An Jiaotong University School of Computer Science and Technology Xi'an China Xi'An Jiaotong University National Engineering Lab For Big Data Analytics Xi'an China Xi'An Jiaotong University School of Continuing Education Xi'an China

ISBN: (纸本)9781665438582

Multi-turn dialogue generation aims to generate natural and fluent responses that should be consistent with multiple consecutive utterances history. It is a more challenging task compared to its single-turn counterpart since it requires the model to capture the topic drift along with the multi-turn dialogue history. In this paper, we propose a multi-turn dialogue generation model which incorporates topic drift aware information into a hierarchical encoder-decoder framework to generate coherent responses. This model first utilizes a Convolutional Neural Network (CNN) based topic model to obtain the topic representation of each utterance. Then a topic drift model is employed to encode the sequential topics of multi-turn dialogue history to infer the topic of response. During the response generation, a specially designed topic drift aware generator is proposed to dynamically balance the impact of the inferred topic of response and local word structure. Fur-thermore, we employ multi-task learning to optimize the topic drift model and dialogue generation simultaneously. Extensive experimental results on two benchmark datasets (i.e. Cornell Movie Dialog Corpus and Ubuntu Dialogue dataset) indicate that our proposed model can generate more coherent responses, and significantly outperform other dialogue generation models. © 2021 IEEE.

关键词： Learning systems

来源：评论

学校读者我要写书评

暂无评论

Wind-Bell Index: Towards Ultra-Fast Edge Query for Graph databases

Wind-Bell Index: Towards Ultra-Fast Edge Query for Graph Dat...

引用

International Conference on data engineering

作者： Rui Qiu Yi Ming Yisen Hong Haoyu Li Tong Yang School of Computer Science and National Engineering Laboratory for Big Data Analysis Technology and Application Peking University Beijing China Academy for Advanced Interdisciplinary Studies Peking University Peng Cheng Laboratory Shenzhen China

Graphs are good at presenting relational and structural information, making it powerful in the representation of various data. For the efficient storage and processing of graph-like data, graph databases have been rapidly developed and extensively studied. However, graph databases mostly use adjacency lists as their basic data structure (e.g., Neo4j), which could result in poor performance of edge due to the skewed degree distribution of *** design the Wind-Bell Index to address this problem. Wind-Bell Index is a memory-efficient index data structure, which can be attached to existing graph databases to speed up the edge. We have fully implemented our data structure in Neo4j, the most popular graph database today, and conduct theoretical and experimental analysis to evaluate the performance. Theoretical results prove the high query efficiency of our algorithm. And experimental results show that the average edge query speed is increased by hundreds of times compared with the original query interface of Neo4j. We believe that the excellent performance and scalability of Wind-Bell Index make it suitable for the application in a variety of graph databases.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A consensus model for linguistic group decision making considering emotion analysis

引用

Procedia computer science 2023年 221卷 1235-1241页

作者： Mengru Xu Fuying Jing Xiangrui Chao Enrique Herrera-viedma School of Management Science and Engineering Chongqing Technology and Business University Chongqing 400067 China National Research Base of Intelligent Manufacturing Service Chongqing Technology and Business University Chongqing 400067 China School of Business Sichuan University Chengdu 610065 China Andalusian Research Institute in Data Science and Computational Intelligence Department of Computer Science and Artificial Intelligence University of Granada Granada 18071 Spain

In a problem of linguistic group decision-making (GDM), various emotional characteristics of decision-makers’ (DMs) behind their preference series, which is defined as emotion factor, can impact consensus reaching process. As words mean different things to different people, this study comprehensively proposes a novel consensus model with emotion factors based on personalized individual semantics. An improved grey clustering method facilitates to obtain emotion factors. Finally, the proposal model can provide more accurate judgments for moderator's management and further promote consensus.

关键词： Emotion factors linguistic group decision-making personalized individual semantics grey clustering

来源：评论

学校读者我要写书评

暂无评论

Learning Human-Written Commit Messages to Document Code Changes

引用

Journal of computer science & Technology 2020年第6期35卷 1258-1277页

作者： Yuan Huang Nan Jia Hao-Jie Zhou Xiang-Ping Chen Zi-Bin Zheng Ming-Dong Tang National Engineering Research Center of Digital Life School of Data and Computer ScienceSun Yat-sen University Guangzhou 510006China School of Information Engineering Hebei GEO UniversityShijiazhuang 050031China Guangdong Key Laboratory for Big Data Analysis and Simulation of Public Opinion School of Communication and DesignSun Yat-sen UniversityGuangzhou 510006China School of Information Science and Technology Guangdong University of Foreign StudiesGuangzhou 510006China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou 510006China

Commit messages are important complementary information used in understanding code changes. To address message scarcity, some work is proposed for automatically generating commit messages. However, most of these approaches focus on generating summary of the changed software entities at the superficial level, without considering the intent behind the code changes (e.g., the existing approaches cannot generate such message:"fixing 'null' pointer exception"). Considering developers often describe the intent behind the code change when writing the messages, we propose ChangeDoc, an approach to reuse existing messages in version control systems for automatical commit message generation. Our approach includes syntax, semantic, pre-syntax, and pre-semantic similarities. For a given commit without messages, it is able to discover its most similar past commit from a large commit repository, and recommend its message as the message of the given commit. Our repository contains half a million commits that were collected from SourceForge. We evaluate our approach on the commits from 10 projects. The results show that 21.5% of the recommended messages by ChangeDoc can be directly used without modification, and 62.8% require minor modifications. In order to evaluate the quality of the commit messages recommended by ChangeDoc, we performed two empirical studies involving a total of 40 participants (10 professional developers and 30 students). The results indicate that the recommended messages are very good approximations of the ones written by developers and often include important intent information that is not included in the messages generated by other tools.

关键词： commit message recommendation code syntax similarity code semantic similarity code change comprehension

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：