2020 International Conference on Machine Learning and Cybernetics (ICMLC)最新文献_第2页

Behavioral Decision Makings: Reconciling Behavioral Economics and Decision Systems 行为决策:调和行为经济学和决策系统

2020 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2020-12-02 DOI: 10.1109/ICMLC51923.2020.9469538

J. Chai

{"title":"Behavioral Decision Makings: Reconciling Behavioral Economics and Decision Systems","authors":"J. Chai","doi":"10.1109/ICMLC51923.2020.9469538","DOIUrl":"https://doi.org/10.1109/ICMLC51923.2020.9469538","url":null,"abstract":"Decision-making (DM) contains three main tasks: ranking, classification, and choice, which accompanies with the development of human being. Ranking and classification are two fundamental DM tasks that have been well studied in fields of information systems, computer sciences, and business analytics. Decision systems are the typical field for this branch. Another branch is choice that has been formally studied in economics. This paper reconciles the substances of decision systems and economics. As a result, a new field, behavioral decision making (BDM), is put forward and outstanding. We review noteworthy transitions from neoclassical economics to behavioral economics in a historical perspective. We examine the principles of economic and psychology into decision systems researches. Behavioral and experimental economics advocate bounded rationality and fairness, which challenge absolute rationality and self-interest assumed by neoclassical economics. BDM could be the best direction of bridging neoclassical sides and behavioral sides, which bring new insights into the communities of information management and systems. A full picture of transitions from people to systems (machines) is exhibited after incorporating behavioral and psychological principles.","PeriodicalId":170815,"journal":{"name":"2020 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"262 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114004623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Operating System Classification: A Minimalist Approach 操作系统分类:一种极简方法

2020 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2020-12-02 DOI: 10.1109/ICMLC51923.2020.9469571

Kyle Millar, A. Cheng, Hong-Gunn Chew, C. Lim

{"title":"Operating System Classification: A Minimalist Approach","authors":"Kyle Millar, A. Cheng, Hong-Gunn Chew, C. Lim","doi":"10.1109/ICMLC51923.2020.9469571","DOIUrl":"https://doi.org/10.1109/ICMLC51923.2020.9469571","url":null,"abstract":"Operating system (OS) classification is of growing importance to network administrators and cybersecurity analysts alike. The composition of OSs on a network allows for a better quality of device management to be achieved. Additionally, it can be used to identify devices that pose a security risk to the network. However, the sheer number and diversity of OSs that comprise modern networks have vastly increased this management complexity. We leverage insights from social networking theory to provide an encryption-invariant OS classification technique that is quick to train and widely deployable on various network configurations. In particular, we show how an affiliation graph can be used as an input to a machine learning classifier to predict the OS of a device using only the IP addresses for which the device communicates with.We examine the effectiveness of our approach through an empirical analysis of 498 devices on a university campus’ wireless network. In particular, we show our methodology can classify different OS families (i.e., Apple, Windows, and Android OSs) with an accuracy of 99.3%. Furthermore, we extend this study by: 1) examining distinct OSs (e.g., iOS, OS X, and Windows 10); 2) investigating the interval of time required to make an accurate prediction; and, 3) determining the effectiveness of our approach after six months.","PeriodicalId":170815,"journal":{"name":"2020 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115917198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Color-Dust: A Data Visualization Application of Image Color Based on K-Means Algorithm Color- dust:基于K-Means算法的图像颜色数据可视化应用

2020 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2020-12-02 DOI: 10.1109/ICMLC51923.2020.9469553

Ruixan Yang, Chunfang Li, Yujun Wen

引用次数: 0

Metric Learning Based Similarity Measure For Attribute Description Identification Of Energy Data 基于度量学习的能源数据属性描述识别相似度度量

2020 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2020-12-02 DOI: 10.1109/ICMLC51923.2020.9469547

Guo-Jing Liu, Hao Chen, Lin-Yu Wang, Di Zhu

引用次数: 0

A Fast Reduction Algorithm with Attribute Pre-Sort Based on Neighborhood Rough Set 基于邻域粗糙集的属性预排序快速约简算法

2020 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2020-12-02 DOI: 10.1109/ICMLC51923.2020.9469591

Meng Hu, Eric C. C. Tsang, Yanting Guo, Weihua Xu, De-gang Chen

引用次数: 2

A Watershed-Based Intelligent Scissors Approach for Interactive Semi-Automated Pulmonary Lobes Segmentation 基于分水岭的交互式半自动肺叶分割智能剪子方法

2020 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2020-12-02 DOI: 10.1109/ICMLC51923.2020.9469543

Qiang Li, Yan Kang

引用次数: 2

Dual-Path Model for Person Re-Identification Under Cloth Changing 换布下人物再识别的双路径模型

2020 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2020-12-02 DOI: 10.1109/ICMLC51923.2020.9469545

Junhao Zheng, Xiaoman Hu, Tianyi Xiang, P. Chan

{"title":"Dual-Path Model for Person Re-Identification Under Cloth Changing","authors":"Junhao Zheng, Xiaoman Hu, Tianyi Xiang, P. Chan","doi":"10.1109/ICMLC51923.2020.9469545","DOIUrl":"https://doi.org/10.1109/ICMLC51923.2020.9469545","url":null,"abstract":"Most of the existing person re-identification (ReID) methods relies heavily on a person's clothes since clothing information is the clear and remarkable visual feature when the face of a person is unclear. However, in reality, people does not always wear the same cloth across camera views. Even worse, an adversary may change the clothes aiming to evade the identification. Some studies confirms that clothes changing downgrades the existing ReID methods significantly. The current ReID method considering clothes-changing does not fully utilize the person discriminant features, which may reduce its accuracy. This paper presents a dual-path model to learn the robust features under clothes changing and also the discriminant features for ReID from a RGB image and its contour sketch image respectively. The appearance and shape features of a person extracted by the two branches of our model are then combined to make a decision. The clothing information is eliminated from the appearance features by encouraging the similarity between the learned appearance and shape features. The experimental results on the PRCC dataset demonstrate that our model achieves higher performance under clothes changing compared to state-of-the-art ReID methods.","PeriodicalId":170815,"journal":{"name":"2020 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125256699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Prediction of Coronary Artery Disease using Electrocardiography: A Machine Learning Approach 使用心电图预测冠状动脉疾病:一种机器学习方法

2020 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2020-12-02 DOI: 10.1109/ICMLC51923.2020.9469585

Gautam Phadke, M. Rajati, Leena Phadke

引用次数: 0

A Study on Two-Stage Approach for Traffic Sign Recognition: Few-to-Many or Many-to-Many? 交通标志识别的两阶段方法研究:少对多还是多对多?

2020 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2020-12-02 DOI: 10.1109/ICMLC51923.2020.9469574

Meng-Huan Hsieh, Qiang Zhao

{"title":"A Study on Two-Stage Approach for Traffic Sign Recognition: Few-to-Many or Many-to-Many?","authors":"Meng-Huan Hsieh, Qiang Zhao","doi":"10.1109/ICMLC51923.2020.9469574","DOIUrl":"https://doi.org/10.1109/ICMLC51923.2020.9469574","url":null,"abstract":"Needless to say, traffic sign recognition (TSR) is important for safety driving. A TSR system can make the driver more aware of the road situation and condition, and thus can reduce traffic accidents. A TSR system contains mainly two parts, one for detection and another for classification. Recently, deep learners such as You Only Look One (YOLO) and Single Shot Multi-Box Detector (SSD) have been proposed for implementing these two parts together. However, since there are many different traffic signs, training a good model is usually time consuming. In this study, we investigate the efficiency/efficacy of two different two-stage approaches for TSR. The first approach is a few-to-many approach, in which YOLO-v3 is used to detect traffic signs based on their shapes and VGG-16 is used to classify the signs into detailed classes. The second one is a many-to-many approach, in which traffic signs are detected and classified by YOLO-v3, and VGG-16 is used to correct signs miss-classified by YOLO-v3. Experiment results show that, the average accuracy of the many-to-many approach is 93.98% and that of the few-to-many approach is 88.29% for the German Traffic Sign Detection Benchmark dataset. Compared with the YOLO-v3 alone approach, the many-to-many approach has a 23.08% gain but the few-to-many approach has only a 6.2% gain.","PeriodicalId":170815,"journal":{"name":"2020 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129600920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ICMLC 2020 Cover Page ICMLC 2020封面

2020 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2020-12-02 DOI: 10.1109/icmlc51923.2020.9469535

引用次数: 0