检索结果-内蒙古大学图书馆

11th international conference on Computer Supported Education, CSEDU 2019

作者： Heggernes, Tarjei Bergfjord, Ole Jakob Department of Business Administration Western Norway University of Applied Sciences Bergen Norway

ISBN: (纸本)9789897583674

We argue that better use of "digital footprints" (data generated from students’ learning activities) could be used to improve the traditional lecture. We point out some potentially important data sources, and briefly discuss how data from each of these sources can contribute to better learning. Finally, we argue that even if this development is possible, it will require changes – certainly changed priorities among faculty, but also probably recruitment of new types of expertise, for instance data scientists. © 2019 by SCITEPRESS – science and Technology Publications, Lda. All rights reserved

关键词： E-learning

来源：评论

学校读者我要写书评

暂无评论

Generative Modeling for Small-data Object Detection 17

Generative Modeling for Small-Data Object Detection

引用

IEEE/CVF international conference on Computer Vision (ICCV)

作者： Liu, Lanlan Muelly, Michael Deng, Jia Pfister, Tomas Li, Jia Univ Michigan Ann Arbor MI 48109 USA Google Cloud AI Mountain View CA USA Princeton Univ Princeton NJ 08544 USA Stanford Univ Stanford CA 94305 USA Google Mountain View CA 94043 USA

ISBN: (纸本)9781728148038

This paper explores object detection in the small data regime, where only a limited number of annotated bounding boxes are available due to data rarity and annotation expense. This is a common challenge today with machine learning being applied to many new tasks where obtaining training data is more challenging, e.g. in medical images with rare diseases that doctors sometimes only see once in their life-time. In this work we explore this problem from a generative modeling perspective by learning to generate new images with associated bounding boxes, and using these for training an object detector. We show that simply training previously proposed generative models does not yield satisfactory performance due to them optimizing for image realism rather than object detection accuracy. To this end we develop a new model with a novel unrolling mechanism that jointly optimizes the generative model and a detector such that the generated images improve the performance of the detector. We show this method outperforms the state of the art on two challenging datasets, disease detection and small data pedestrian detection, improving the average precision on NIH Chest X-ray by a relative 20% and localization accuracy by a relative 50%.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

A new reinforcement learning-based framework for unbiased autonomous software systems 8

A new reinforcement learning-based framework for unbiased au...

引用

8th international conference on Modeling Simulation and applied Optimization, ICMSAO 2019

作者： Sulaimon, Ismail Abiodun Ghoneim, Ahmed Alrashoud, Mubarak Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia

ISBN: (纸本)9781538676844

From the newsfeeds, the products and services advertised to us, job screening, risk analysis, facial recognition and to the results we get through search engines, human-curated algorithms sitting behind the scenes, are making these decisions. These algorithms sometimes display the choices of those who authored them. Algorithmic bias has always been a minor issue since the advent of computer software, not until now that computer algorithms are deeply rooted in our daily lives through smart devices, intelligent software solutions and Autonomous System. We are at the edge of leaving our critical decisions in the hands of these intelligent creations of ours. Whereas, the biases in the algorithms used to develop them and the biases in the data they were trained with are obviously still in existence. This research is aimed at adapting the existing bias detection mechanism to ensure fairness in the decision-making process of autonomous software systems. The final solution is in the form of a software module which provides access for auditing decision process of machine learning powered autonomous software systems. This, in turn, ensures fairness in the decision process of autonomous software systems. © 2019 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Improvement of Mathematical Problem Solving Ability Through Problem Based learning Model In applied Mathematics Course II In Industrial Engineering 1

Improvement of Mathematical Problem Solving Ability Through ...

引用

1st South Aceh international conference on Engineering and Technology (SAICOET)

作者： Rusnanda, Resky Safwandi Polytech South Aceh Merdeka StKomplek Reklamasi Pantai Tapaktuan City 23751 Indonesia

Problem based learning model is applicable in applied Mathematics II course in order to obtain better students' problem-solving skill in mathematic. In this research, one can learn the differences of student performance to solve problem by problem-based learning compared to traditional learning. Besides, observation has been done to student activities during class session. data analysis shows significant of a = 0.05 gained t(count) = 2.16 and t(Table) = 1.72 in other hand teaaat > ttabie, one can conclude that problem-based learning increase student performance in solving problem compared to those in control group in applied Mathematics II course, Study Program of Industrial Engineering Polytechnic of South Aceh. The student activity was rate at 69.64% average.

关键词： problem solving ability problem based learning applied mathematics

来源：评论

学校读者我要写书评

暂无评论

Early Detecting In-Hospital Cardiac Arrest Based on machine learning on Imbalanced data 7

Early Detecting In-Hospital Cardiac Arrest Based on Machine ...

引用

7th IEEE international conference on Healthcare Informatics (ICHI)

作者： Chang, Hsiao-Ko Wu, Cheng-Tse Liu, Ji-Han Lim, Wee Shin Wang, Hui-Chih Chiu, Shu-I Jang, Jyh-Shing Roger Natl Taiwan Univ Dept Comp Sci & Informat Engn Taipei Taiwan Natl Taiwan Univ Grad Inst Networking & Multimedia Taipei Taiwan Natl Taiwan Univ Hosp Dept Emergency Med Taipei Taiwan

ISBN: (纸本)9781538691380

In-hospital cardiac arrest (IHCA) diminish the survival rate of patients, despite most of the IHCA cases are preventable. More than 54% IHCA patient had abnormal clinical manifestation before they suffered a cardiac arrest. If appropriate steps were taken, patients' survival rate would be higher and medical expense would be decreased. This paper proposes a novel approach to detect IHCA before the event occurred. We construct two types of shifting windows (corresponding to two tasks) that allow machine learning to be applied for our dataset which is severely imbalanced. The results show that our approach can effectively handle the imbalanced dataset for detecting cardiac arrest. As the selection of performance index, we used the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC). In our experiments, the best classifier is random forest for task 1, with AUROC of 0.88. LSTM is the best for task 2, with AUPRC of 0.71 for the second task.

关键词： Cardiac arrest cardiopulmonary resuscitation imbalanced data classification machine learning prediction

来源：评论

学校读者我要写书评

暂无评论

On Tree-Based Methods for Similarity learning 5th

On Tree-Based Methods for Similarity Learning

引用

5th international conference on machine learning, Optimization, and data science (LOD)

作者： Clemencon, Stephan Vogel, Robin Inst Polytech Paris LTCI Telecom Paris Paris France IDEMIA Courbevoie France

ISBN: (纸本)9783030375997;9783030375980

In many situations, the choice of an adequate similarity measure or metric on the feature space dramatically determines the performance of machine learning methods. Building automatically such measures is the specific purpose of metric/similarity learning. In [21], similarity learning is formulated as a pairwise bipartite ranking problem: ideally, the larger the probability that two observations in the feature space belong to the same class (or share the same label), the higher the similarity measure between them. From this perspective, the ROC curve is an appropriate performance criterion and it is the goal of this article to extend recursive tree-based ROC optimization techniques in order to propose efficient similarity learning algorithms. The validity of such iterative partitioning procedures in the pairwise setting is established by means of results pertaining to the theory of U-processes and from a practical angle, it is discussed at length how to implement them by means of splitting rules specifically tailored to the similarity learning task. Beyond these theoretical/methodological contributions, numerical experiments are displayed and provide strong empirical evidence of the performance of the algorithmic approaches we propose.

关键词： Metric-learning Rate bound analysis Similarity learning Tree-based algorithms U-processes

来源：评论

学校读者我要写书评

暂无评论

Improving Understanding of EEG Measurements Using Transparent machine learning Models 8th

Improving Understanding of EEG Measurements Using Transparen...

引用

8th international conference on Health Information science (HIS)

作者： Roadknight, Chris Zong, Guanyu Rattadilok, Prapa Univ Nottingham Ningbo Campus Ningbo Peoples R China

ISBN: (纸本)9783030329624;9783030329617

Physiological datasets such as Electroencephalography (EEG) data offer an insight into some of the less well understood aspects of human physiology. This paper investigates simple methods to develop models of high level behavior from low level electrode readings. These methods include using neuron activity based pruning and large time slices of the data. Both approaches lead to solutions whose performance and transparency are superior to existing methods.

关键词： Deep learning Physiological data CAPing

来源：评论

学校读者我要写书评

暂无评论

Recognising Irish Sign Language Using Electromyography 17

Recognising Irish Sign Language Using Electromyography

引用

17th international conference on Content-Based Multimedia Indexing (IEEE CBMI)

作者： Galea, Laura Cristina Smeaton, Alan F. Dublin City Univ Dublin 9 Ireland Dublin City Univ Insight Ctr Data Analyt Dublin 9 Ireland

ISBN: (纸本)9781728146737

Sign language is the non-verbal communication used by people with hearing and speaking impairments. The automatic recognition of sign languages is usually based on video analysis of the signer though this is difficult when considering different light levels or the surrounding environment. The work in this paper uses electromyography (EMG) and focuses on letters of the Irish Sign Language (ISL) alphabet. EMG is the recording of the electrical activity produced to stimulate movement in the skeletal muscles. We capture muscle signals and inertial movement data using the Thalmic MYO armband and, in real time, recognise the ISL alphabet. Our implementation is based on signal processing, feature extraction and machine learning. The only input required to translate the ISL gestures are EMG and movement data, thus our approach is usable in scenarios where using video for automatic recognition video is not possible.

关键词： Sign language recognition EMG inertial movement machine learning

来源：评论

学校读者我要写书评

暂无评论

Editorial for the Special Issue on Selected Papers from the "2019 international Symposium on Remote Sensing"

引用

REMOTE SENSING 2020年第12期12卷 1947-1947页

作者： Tsai, Fuan Lin, Chao-Hung Chen, Walter W. Jaw, Jen-Jer Tseng, Kuo-Hsin Natl Cent Univ Ctr Space & Remote Sensing Res 300 Zhongda Rd Taoyuan 32001 Taiwan Natl Cheng Kung Univ Dept Geomat 1 Daxue Rd Tainan 701 Taiwan Natl Taipei Univ Technol Dept Civil Engn 1Sec 3Chung Hsiao E Rd Taipei 10608 Taiwan Natl Taiwan Univ Dept Civil Engn 1Sec 4Roosevelt Rd Taipei 10617 Taiwan

The 2019 international Symposium on Remote Sensing (ISRS-2019) took place in Taipei, Taiwan from 17 to 19 April 2019. ISRS is one of the distinguished conferences on the photogrammetry, remote sensing and spatial information sciences, especially in East Asia. More than 220 papers were presented in 37 technical sessions organized at the conference. This Special Issue publishes a limited number of featured peer-reviewed papers extended from their original contributions at ISRS-2019. The selected papers highlight a variety of topics pertaining to innovative concepts, algorithms and applications with geospatial sensors, systems, and data, in conjunction with emerging technologies such as artificial intelligence, machine leaning and advanced spatial analysis algorithms. The topics of the selected papers include the following: the on-orbit radiometric calibration of satellite optical sensors, environmental characteristics assessment with remote sensing, machine learning-based photogrammetry and image analysis, and the integration of remote sensing and spatial analysis. The selected contributions also demonstrate and discuss various sophisticated applications in utilizing remote sensing, geospatial data, and technologies to address different environmental and societal issues. Readers should find the Special Issue enlightening and insightful for understanding state-of-the-art remote sensing and spatial information science research, development and applications.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Empirical Study on Malicious URL Detection Using machine learning 15th

Empirical Study on Malicious URL Detection Using Machine Lea...

引用

15th international conference on Distributed Computing and Internet Technology (ICDCIT)

作者： Patgiri, Ripon Katari, Hemanth Kumar, Ronit Sharma, Dheeraj Natl Inst Technol Silchar Silchar 788010 Assam India

ISBN: (纸本)9783030053666;9783030053659

In this paper, the malicious URLs detection is treated as a binary classification problem and performance of several well-known classifiers are tested with test data. The algorithms Random Forests and support Vector machine (SVM) are studied in particular which attain a high accuracy. These algorithms are used for training the dataset for classification of good and bad URLs. The dataset of URLs is divided into training and test data in 60:40, 70:30 and 80:20 ratios. Accuracy of Random Forests and SVMs is calculated for several iterations for each split ratio. According to the results, the split ratio 80:20 is observed as more accurate split and average accuracy of Random Forests is more than SVMs. SVM is observed to be more fluctuating than Random Forests in accuracy.

关键词： Malicious URL detection Network security machine learning Random Forest Suport vector machine SVM

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：