2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA)最新文献_第8页

A Hybrid Recommender System for Patient-Doctor Matchmaking in Primary Care 一种用于初级保健医患匹配的混合推荐系统

2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) Pub Date : 2018-08-09 DOI: 10.1109/DSAA.2018.00062

Qiwei Han, Mengxin Ji, Inigo Martinez de Rituerto de Troya, Manas Gaur, Leid Zejnilovic

引用次数: 39

Starting Movement Detection of Cyclists Using Smart Devices 使用智能设备的自行车启动运动检测

2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) Pub Date : 2018-08-08 DOI: 10.1109/DSAA.2018.00042

Maarten Bieshaar, Malte Depping, Jan Schneegans, B. Sick

{"title":"Starting Movement Detection of Cyclists Using Smart Devices","authors":"Maarten Bieshaar, Malte Depping, Jan Schneegans, B. Sick","doi":"10.1109/DSAA.2018.00042","DOIUrl":"https://doi.org/10.1109/DSAA.2018.00042","url":null,"abstract":"In near future, vulnerable road users (VRUs) such as cyclists and pedestrians will be equipped with smart devices and wearables which are capable to communicate with intelligent vehicles and other traffic participants. Road users are then able to cooperate on different levels, such as in cooperative intention detection for advanced VRU protection. Smart devices can be used to detect intentions, e.g., an occluded cyclist intending to cross the road, to warn vehicles of VRUs, and prevent potential collisions. This article presents a human activity recognition approach to detect the starting movement of cyclists wearing smart devices. We propose a novel two-stage feature selection procedure using a score specialized for robust starting detection reducing the false positive detections and leading to understandable and interpretable features. The detection is modelled as a classification problem and realized by means of a machine learning classifier. We introduce an auxiliary class, that models starting movements and allows to integrate early movement indicators, i.e., body part movements indicating future behaviour. In this way we improve the robustness and reduce the detection time of the classifier. Our empirical studies with real-world data originating from experiments which involve 49 test subjects and consists of 84 starting motions show that we are able to detect the starting movements early. Our approach reaches an F1-score of 67 % within 0.33 s after the first movement of the bicycle wheel. Investigations concerning the device wearing location show that for devices worn in the trouser pocket the detector has less false detections and detects starting movements faster on average. % compared to reference detector involving all wearing locations. We found that we can further improve the results when we train distinct classifiers for different wearing locations. In this case we reach an F1-score of 94 % with a mean detection time of 0.34 s for the device worn in the trouser pocket.","PeriodicalId":208455,"journal":{"name":"2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122005780","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: 6

The Economic Value of Neighborhoods: Predicting Real Estate Prices from the Urban Environment 社区的经济价值:从城市环境预测房地产价格

2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) Pub Date : 2018-08-07 DOI: 10.1109/DSAA.2018.00043

Marco De Nadai, B. Lepri

引用次数: 26

Hoeffding Trees with Nmin Adaptation 具有Nmin适应性的Hoeffding树

2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) Pub Date : 2018-08-03 DOI: 10.1109/DSAA.2018.00017

E. García-Martín, Niklas Lavesson, Håkan Grahn, E. Casalicchio, V. Boeva

{"title":"Hoeffding Trees with Nmin Adaptation","authors":"E. García-Martín, Niklas Lavesson, Håkan Grahn, E. Casalicchio, V. Boeva","doi":"10.1109/DSAA.2018.00017","DOIUrl":"https://doi.org/10.1109/DSAA.2018.00017","url":null,"abstract":"Machine learning software accounts for a significant amount of energy consumed in data centers. These algorithms are usually optimized towards predictive performance, i.e. accuracy, and scalability. This is the case of data stream mining algorithms. Although these algorithms are adaptive to the incoming data, they have fixed parameters from the beginning of the execution. We have observed that having fixed parameters lead to unnecessary computations, thus making the algorithm energy inefficient. In this paper we present the nmin adaptation method for Hoeffding trees. This method adapts the value of the nmin parameter, which significantly affects the energy consumption of the algorithm. The method reduces unnecessary computations and memory accesses, thus reducing the energy, while the accuracy is only marginally affected. We experimentally compared VFDT (Very Fast Decision Tree, the first Hoeffding tree algorithm) and CVFDT (Concept-adapting VFDT) with the VFDT-nmin (VFDT with nmin adaptation). The results show that VFDT-nmin consumes up to 27% less energy than the standard VFDT, and up to 92% less energy than CVFDT, trading off a few percent of accuracy in a few datasets.","PeriodicalId":208455,"journal":{"name":"2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125521537","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: 8

Gradient Reversal against Discrimination: A Fair Neural Network Learning Approach 反歧视梯度反转:一种公平的神经网络学习方法

2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) Pub Date : 2018-07-01 DOI: 10.1109/DSAA.2018.00029

Edward Raff, Jared Sylvester

引用次数: 28

A Multi-Task Deep Learning Architecture for Maritime Surveillance Using AIS Data Streams 基于AIS数据流的海上监控多任务深度学习架构

2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) Pub Date : 2018-06-06 DOI: 10.1109/DSAA.2018.00044

Duong Nguyen, Rodolphe Vadaine, G. Hajduch, R. Garello, Ronan Fablet

引用次数: 76

Explaining Explanations: An Overview of Interpretability of Machine Learning 解释解释:机器学习可解释性概述

2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) Pub Date : 2018-05-31 DOI: 10.1109/DSAA.2018.00018

Leilani H. Gilpin, David Bau, Ben Z. Yuan, Ayesha Bajwa, Michael A. Specter, Lalana Kagal

{"title":"Explaining Explanations: An Overview of Interpretability of Machine Learning","authors":"Leilani H. Gilpin, David Bau, Ben Z. Yuan, Ayesha Bajwa, Michael A. Specter, Lalana Kagal","doi":"10.1109/DSAA.2018.00018","DOIUrl":"https://doi.org/10.1109/DSAA.2018.00018","url":null,"abstract":"There has recently been a surge of work in explanatory artificial intelligence (XAI). This research area tackles the important problem that complex machines and algorithms often cannot provide insights into their behavior and thought processes. XAI allows users and parts of the internal system to be more transparent, providing explanations of their decisions in some level of detail. These explanations are important to ensure algorithmic fairness, identify potential bias/problems in the training data, and to ensure that the algorithms perform as expected. However, explanations produced by these systems is neither standardized nor systematically assessed. In an effort to create best practices and identify open challenges, we describe foundational concepts of explainability and show how they can be used to classify existing literature. We discuss why current approaches to explanatory methods especially for deep neural networks are insufficient. Finally, based on our survey, we conclude with suggested future research directions for explanatory artificial intelligence.","PeriodicalId":208455,"journal":{"name":"2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127841297","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: 1411

Forecasting Retweet Count during Elections Using Graph Convolution Neural Networks 使用图卷积神经网络预测选举期间的转发数

2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) Pub Date : 2018-05-31 DOI: 10.1109/DSAA.2018.00036

Raghavendran Vijayan, G. Mohler

{"title":"Forecasting Retweet Count during Elections Using Graph Convolution Neural Networks","authors":"Raghavendran Vijayan, G. Mohler","doi":"10.1109/DSAA.2018.00036","DOIUrl":"https://doi.org/10.1109/DSAA.2018.00036","url":null,"abstract":"A retweet refers to sharing a tweet posted by another user on Twitter and is primary way information spreads on the Twitter network. Political parties use Twitter extensively as a part of their campaign to promote their presence, announce their propaganda, and at times debating with opponents. In this work we consider the problem of early prediction of the final retweet count using information from the network during the first several minutes after a post is made. Such predictions are useful for ranking and promoting posts and also can be used in combination with fake news detection. From a machine learning perspective, the task can be viewed as a regression problem. We introduce a novel graph convolution neural network for forecasting retweet count that combines network level features through graph convolution layers as well as tweet level features at a higher dense layer in the network. We first will provide an overview of the graph convolution network architecture and then perform several experiments on Twitter data collected during presidential elections in South Africa (2014) and Kenya (2013). We show that the model outperforms baseline models including a feed forward neural network and the popular point process based model SEISMIC.","PeriodicalId":208455,"journal":{"name":"2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131188995","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: 12

Airpred: A Flexible R Package Implementing Methods for Predicting Air Pollution Airpred:一个灵活的R包实现预测空气污染的方法

2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) Pub Date : 2018-05-29 DOI: 10.1109/DSAA.2018.00074

M. Sabath, Q. Di, D. Braun, J. Schwartz, F. Dominici, C. Choirat

引用次数: 3

Learning to Make Predictions on Graphs with Autoencoders 学习用自动编码器对图形进行预测

2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) Pub Date : 2018-02-23 DOI: 10.1109/DSAA.2018.00034

Phi Vu Tran

引用次数: 54