2019 20th IEEE International Conference on Mobile Data Management (MDM)最新文献_第3页

Action Recognition from Low-Resolution Infrared Sensor for Indoor use: A Comparative Study between Deep Learning and Classical Approaches 室内低分辨率红外传感器的动作识别:深度学习与经典方法的比较研究

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-10 DOI: 10.1109/MDM.2019.00-11

Félix Polla, H. Laurent, B. Emile

{"title":"Action Recognition from Low-Resolution Infrared Sensor for Indoor use: A Comparative Study between Deep Learning and Classical Approaches","authors":"Félix Polla, H. Laurent, B. Emile","doi":"10.1109/MDM.2019.00-11","DOIUrl":"https://doi.org/10.1109/MDM.2019.00-11","url":null,"abstract":"In recent years, automatic action recognition has attracted a lot of attention in the field of computer vision. In this paper, action recognition in an indoor environment using low-resolution sensor is considered. In this environment, several studies have been carried out in visible imagery. Despite impressive performances, the main pitfall faced by the techniques developed is a marked restraint of the users to be filmed. To cope with the problems of personal identity revealed during the surveillance, we opted within the CoCAPS project for using a low resolution (64 x 64 pixels) infrared sensor with ceiling position which guarantees the respect of the intimacy of the person. Defined in collaboration with the industrial partners involved in the CoCAPS project, the scenarios targeted concern office situations and 7 classes of action have been selected, namely: no action, restlessness, sitting down, standing up, turning sitting on a seat, slow walking, fast walking. Classical approaches such as those based on the computation of shape descriptors (such as geometric and Color Histogram of Oriented Phases (CHOP)) extracted from Motion History Image (MHI) are investigated to represent action video sequences. Within this first group of classical approaches, the performance of a proposed model based on statistical attributes constructed from the tracking of centers of gravity of segmented forms is also presented. The comparative study is then completed by considering other models extracted from deep learning literature (convolutional neural networks (3D-CNN), Long Short Term Memory (LSTM)). The results obtained from the comparative study show that the proposed model is very competitive and provides promising results (83% of f-score).","PeriodicalId":241426,"journal":{"name":"2019 20th IEEE International Conference on Mobile Data Management (MDM)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131933385","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: 2

An Automated Framework for Explaining Facts Extracted From Mobility Datasets 从移动数据集中提取事实的自动解释框架

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-10 DOI: 10.1109/MDM.2019.00-48

Anique Tahir, Yuhan Sun, Mohamed Sarwat

{"title":"An Automated Framework for Explaining Facts Extracted From Mobility Datasets","authors":"Anique Tahir, Yuhan Sun, Mohamed Sarwat","doi":"10.1109/MDM.2019.00-48","DOIUrl":"https://doi.org/10.1109/MDM.2019.00-48","url":null,"abstract":"When a data scientist analyzes mobility data (e.g., using a data visualization tool), she may find out some interesting facts in the dataset. An example of a fact can be: \"The number of Taxi trips in NYC on January 23, 2016, dropped drastically as compared to other days of the same month\". However, the data scientist may be left clueless if they cannot find a crisp explanation to such a fact. Furthermore, the tedious task of finding an explanation by manually scraping the data becomes even impossible with big data. Existing techniques are designed for non-spatial data which cannot be applied to spatial data because it does not consider the spatial proximity. In this paper, we propose an automatic framework which guides the data scientist to explain the fact discovered from mobility data. Our approach expands on the aggravation and intervention techniques while using spatial partitioning/clustering to improve explanations for spatial data. Experiments show that the proposed approach outperforms the state-of-the-art approaches in finding the explanation for facts extracted from NYC taxi real mobility dataset.","PeriodicalId":241426,"journal":{"name":"2019 20th IEEE International Conference on Mobile Data Management (MDM)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132950362","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

Mobile Data Collection and Analysis with Local Differential Privacy 基于局部差分隐私的移动数据采集与分析

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-10 DOI: 10.1109/MDM.2019.00-80

Ninghui Li, Qingqing Ye

引用次数: 10

A Spatial Insight for UGC Apps: Fast Similarity Search on Keyword-Induced Point Groups UGC应用的空间洞察:关键字诱导点群的快速相似性搜索

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-10 DOI: 10.1109/MDM.2019.00-26

Zhe Li, Yu Li, Man Lung Yiu

{"title":"A Spatial Insight for UGC Apps: Fast Similarity Search on Keyword-Induced Point Groups","authors":"Zhe Li, Yu Li, Man Lung Yiu","doi":"10.1109/MDM.2019.00-26","DOIUrl":"https://doi.org/10.1109/MDM.2019.00-26","url":null,"abstract":"In the era of smartphones, massive data are generated with geo-related info. A large portion of them come from UGC applications (e.g., Twitter, Instagram), where the content provider are users themselves. Such applications are highly attractive for targeted marketing and recommendation, which have been well studied in recommendation system. In this paper, we consider this from a brand new spatial aspect using UGC contents only. To do this we first representing each message as a point with its geo info as its location and then grouping all the points by their keywords to form multiple point groups. We form a similarity search problem that given a query keyword, our problem aims to find k keywords with the most similar distribution of locations. Our case study shows that with similar distribution, the keywords are highly likely to have semantic connections. However, the performance of existing solutions degrades when different point groups have significant overlapping, which frequently happens in UGC contents. We propose efficient techniques to process similarity search on this kind of point groups. Experimental results on Twitter data demonstrate that our solution is faster than the state-of-the-art by up to 6 times.","PeriodicalId":241426,"journal":{"name":"2019 20th IEEE International Conference on Mobile Data Management (MDM)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126867131","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: 2

CAPRIO: Context-Aware Path Recommendation Exploiting Indoor and Outdoor Information CAPRIO:利用室内和室外信息的情境感知路径推荐

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-10 DOI: 10.1109/MDM.2019.000-7

Constantinos Costa, Xiaoyu Ge, Panos K. Chrysanthis

{"title":"CAPRIO: Context-Aware Path Recommendation Exploiting Indoor and Outdoor Information","authors":"Constantinos Costa, Xiaoyu Ge, Panos K. Chrysanthis","doi":"10.1109/MDM.2019.000-7","DOIUrl":"https://doi.org/10.1109/MDM.2019.000-7","url":null,"abstract":"During extreme weather conditions and natural disasters caused by meteorological phenomena, it is imperative to enable navigation that minimizes the outdoor section of recommended paths. Existing indoor-outdoor navigation and localization systems have evolved to support queries like the shortest distance, either outdoor or indoor, with additional constraints. However, most of them work in isolation and do not take into consideration the external natural conditions, like the weather, that an individual may experience walking outside during a polar vortex or heatwave. In this paper, we present CAPRIO, a context-aware path recommendation system whose objectives are two-fold: (i) minimizing outdoor exposure; and (ii) minimizing the distance of the recommended path. We propose a novel graph representation that integrates indoor and outdoor information to discover paths that satisfy outdoor exposure and distance constraints. We measure the efficiency of the proposed solution using two real datasets collected from the University of Pittsburgh and University of Cyprus campuses. We show that we can achieve comparable distance to the state-of-the-art in minimizing outdoor exposure.","PeriodicalId":241426,"journal":{"name":"2019 20th IEEE International Conference on Mobile Data Management (MDM)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114930040","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: 10

Synchronization-Free GPS Spoofing Detection with Crowdsourced Air Traffic Control Data 基于众包空中交通管制数据的无同步GPS欺骗检测

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-10 DOI: 10.1109/MDM.2019.00-49

Gaoyang Liu, Rui Zhang, Chen Wang, Ling Liu

{"title":"Synchronization-Free GPS Spoofing Detection with Crowdsourced Air Traffic Control Data","authors":"Gaoyang Liu, Rui Zhang, Chen Wang, Ling Liu","doi":"10.1109/MDM.2019.00-49","DOIUrl":"https://doi.org/10.1109/MDM.2019.00-49","url":null,"abstract":"GPS-dependent localization, navigation and air traffic control (ATC) applications have had a significant impact on the modern aviation industry. However, the lack of encryption and authentication makes GPS vulnerable to spoofing attacks with the purpose of hijacking aerial vehicles or threatening air safety. In this paper, we propose GPS-Probe, a GPS spoofing detection algorithm that leverages the ATC messages that are periodically broadcasted by aerial vehicles. By continuously analyzing the received signal strength indicator (RSSI) and the timestamps at server (TSS) of the ATC messages, which are monitored by multiple ground sensors, GPS-Probe constructs a machine learning enabled framework to estimate the real position of the target aerial vehicle and to detect whether or not the position data is compromised by GPS spoofing attacks. Unlike existing techniques, GPS-Probe neither requires any updates of the GPS infrastructure nor updates of the GPS receivers. More importantly, it releases the requirement on time synchronization of the ground sensors distributed around the world. Using the real-world ATC data crowdsourced by the OpenSky Network, our experiment results show that GPS-Probe can achieve the detection accuracy and precision, of 81.7% and 85.3% respectively on average, and up to 89.7% and 91.5% respectively at the best.","PeriodicalId":241426,"journal":{"name":"2019 20th IEEE International Conference on Mobile Data Management (MDM)","volume":"308 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134498161","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: 13

A VLOS Compliance Solution to Ground/Aerial Parcel Delivery Problem 地面/空中包裹递送问题的VLOS合规解决方案

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-10 DOI: 10.1109/MDM.2019.00-56

Ji Zhang, Ting Shen, Wenlu Wang, Xunfei Jiang, Wei-Shinn Ku, Min-Te Sun, Yao-Yi Chiang

引用次数: 3

Traffic Incident Detection: A Deep Learning Framework 交通事件检测:一个深度学习框架

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-10 DOI: 10.1109/MDM.2019.00-22

Xiaolin Han

引用次数: 1

Automatic Radio Map Construction Exploiting Mobile Payments 利用移动支付的自动无线地图构建

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-10 DOI: 10.1109/MDM.2019.00-10

Jeonghee Ahn, Dongsoo Han

引用次数: 1

Deep Learning Method for Citywide Crowd Flows Prediction 城市人流量预测的深度学习方法

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-01 DOI: 10.1109/MDM.2019.00-25

Genan Dai

引用次数: 1