2017 IEEE International Conference on AI & Mobile Services (AIMS)最新文献_第2页

MixedWalk: Explore Ahead before Stepping in Mobile Augmented Reality Services MixedWalk:在进入移动增强现实服务之前进行探索

2017 IEEE International Conference on AI & Mobile Services (AIMS) Pub Date : 2017-06-01 DOI: 10.1109/AIMS.2017.17

Jun Lee, Kyoung-Sook Kim, Hirotaka Ogawa, Y. Kwon

{"title":"MixedWalk: Explore Ahead before Stepping in Mobile Augmented Reality Services","authors":"Jun Lee, Kyoung-Sook Kim, Hirotaka Ogawa, Y. Kwon","doi":"10.1109/AIMS.2017.17","DOIUrl":"https://doi.org/10.1109/AIMS.2017.17","url":null,"abstract":"The combination of augmented reality (AR) and location-based services provides mobile users with an intuitive and easy way to access the real-world aware information via their mobile devices, as connecting the physical and cyber world. However, mobile AR interactions limit target objects that are located within a specific distance of the current location of users. In this paper, we propose a new service model, called MixedWalk, for aligning point-of-interest (POI) information with the users' virtual movement in the mobile AR environment. It allows the mobile user to virtually move ahead along the street coupled with the real-time camera scene and identify POIs without actual movements in the real world. We firstly design 'Virtual Movement' layer between the physical-view and information-query layer to provide a natural interaction of the MixedWalk service. Secondly, an efficient processing method is considered for the continuous movement query composed of pie-shape region queries. A pie-shape query is described by a camera location, a viewing direction, and a visible threshold distance. Finally, we demonstrate an implementation of Android applications with the Aenigol dining district in Goyang-si of Gyeonggi-do in Korea.","PeriodicalId":384523,"journal":{"name":"2017 IEEE International Conference on AI & Mobile Services (AIMS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125051658","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

EA-GLES: An Energy-Aware 3D Graphics Library for Mobile Devices EA-GLES:用于移动设备的能量感知3D图形库

2017 IEEE International Conference on AI & Mobile Services (AIMS) Pub Date : 2017-06-01 DOI: 10.1109/AIMS.2017.25

F. Lamberti, G. Paravati, P. Montuschi

引用次数: 0

A Recommendation System for Proactive Health Monitoring Using IoT and Wearable Technologies 基于物联网和可穿戴技术的主动健康监测推荐系统

2017 IEEE International Conference on AI & Mobile Services (AIMS) Pub Date : 2017-06-01 DOI: 10.1109/AIMS.2017.11

Shubhi Asthana, Aly Megahed, R. Strong

{"title":"A Recommendation System for Proactive Health Monitoring Using IoT and Wearable Technologies","authors":"Shubhi Asthana, Aly Megahed, R. Strong","doi":"10.1109/AIMS.2017.11","DOIUrl":"https://doi.org/10.1109/AIMS.2017.11","url":null,"abstract":"Proactive monitoring of one's health could avoid serious diseases as well as better maintain the individual's well-being. In today's Internet of Things (IoT) world, there has been numerous wearable technological devices to monitor/measure different health attributes. With the increasing number of attributes and wearables, it becomes unclear to individuals which ones they should be using. The aim of this paper is to provide a novel recommendation engine for personalized advised wearables and IoT solutions for any given individual. The way the engine works is through first identifying the diseases that this person is at risk of, given his/her attributes and medical history. This is done via analyzing the individual's unstructured medical history using text mining, adding it to his/her structured demographic attributes, and then feeding this data to a machine learning classification model that predicts eventual diseases. Then, we map these diseases to the attributes that need to be measured in order to monitor them. Lastly, we use a mathematical optimization model that we developed to recommend the optimal wearable devices and IoT solutions for the individual. Thus, our solution enables proactive health monitoring and can thus provide a significant human benefit.","PeriodicalId":384523,"journal":{"name":"2017 IEEE International Conference on AI & Mobile Services (AIMS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128366538","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: 52

A Method for Selecting Peer Deals in IT Service Contracts IT服务契约中对等交易的选择方法

2017 IEEE International Conference on AI & Mobile Services (AIMS) Pub Date : 2017-06-01 DOI: 10.1109/AIMS.2017.10

Aly Megahed, Shubhi Asthana, V. Becker, Taiga Nakamura, Kugamoorthy Gajananan

{"title":"A Method for Selecting Peer Deals in IT Service Contracts","authors":"Aly Megahed, Shubhi Asthana, V. Becker, Taiga Nakamura, Kugamoorthy Gajananan","doi":"10.1109/AIMS.2017.10","DOIUrl":"https://doi.org/10.1109/AIMS.2017.10","url":null,"abstract":"To respond to requests for proposals from clients requiring complex Information technology (IT) services, IT service providers have to prepare a solution composed of the multiple services requested by the clients and price that solution. Then, each provider competes in a tender-kind of process trying to convince the client with their solution. Pricing these solutions/deals, using historical and market data, is a complex task that we studied in our previous works. In our prior pricing approaches, we used a simple algorithm for selecting similar historical and market deals to the one we are trying to price, before we mine the data of these deals to estimate the costs of that latter deal. However, there are multiple limitations to that algorithm that we overcome in the novel approach that we present in this paper. These limitations include missing on some similar deals due to the way we chose them. Our new approach involves an iterative algorithm that selects peer deals at different levels until a pre-specified number of deals is determined. We present a proof-of-concept implementation of our approach, using real-world data, to illustrate its efficiency.","PeriodicalId":384523,"journal":{"name":"2017 IEEE International Conference on AI & Mobile Services (AIMS)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123494491","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: 7

Mobile Crowdsensing Services for Tinnitus Assessment and Patient Feedback 耳鸣评估与患者反馈的移动众感服务

2017 IEEE International Conference on AI & Mobile Services (AIMS) Pub Date : 2017-06-01 DOI: 10.1109/AIMS.2017.12

R. Pryss, W. Schlee, B. Langguth, M. Reichert

{"title":"Mobile Crowdsensing Services for Tinnitus Assessment and Patient Feedback","authors":"R. Pryss, W. Schlee, B. Langguth, M. Reichert","doi":"10.1109/AIMS.2017.12","DOIUrl":"https://doi.org/10.1109/AIMS.2017.12","url":null,"abstract":"Assessment of chronic disorders requires new ways of data collection compared to the traditional pen & paper based approaches. For example, tinnitus, the phantom sensation of sound, is a highly prevalent disorder that is difficult to treat, i.e., available treatments are only effective for patient subgroups. In most individuals with tinnitus, loudness and annoyance of tinnitus varies over time. Currently, established assessment methods of tinnitus neither systematically assess this moment-to-moment variability nor environmental factors having an effect on tinnitus loudness and distress. However, information of individual fluctuations and the effect of environmental factors on the tinnitus might represent important information for tinnitus subtyping and for individualized treatment. In this context, a promising approach for collecting ecological valid longitudinal datasets at rather low costs is mobile crowdsensing. In the TrackYourTinnitus project, we developed an advanced mobile crowdsensing platform to reveal more detailed information about the course of tinnitus over time. In this paper, the patient mobile feedback service as a particular component of the platform is presented. It was developed to provide patients with aggregated information about the variation of their tinnitus over time. This mobile feedback service shall help a patient to demystify the tinnitus and to get better control of it, which should facilitate coping with this chronic health condition. As the basic principles and design of this mobile services are also applicable to other chronic disorders, promising perspectives for disorder management and clinical research arise.","PeriodicalId":384523,"journal":{"name":"2017 IEEE International Conference on AI & Mobile Services (AIMS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126382158","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: 51

DNN-Based Approach for Recognition of Human Activity Raw Data in Non-Controlled Environment 非受控环境下基于dnn的人类活动原始数据识别方法

2017 IEEE International Conference on AI & Mobile Services (AIMS) Pub Date : 2017-06-01 DOI: 10.1109/AIMS.2017.26

Hamdi Amroun, M. Temkit, M. Ammi

引用次数: 6

Semantic-Aware IoT Platforms 语义感知物联网平台

2017 IEEE International Conference on AI & Mobile Services (AIMS) Pub Date : 2017-06-01 DOI: 10.1109/AIMS.2017.15

M. B. Alaya, K. Drira, Ghada Gharbi

引用次数: 4

Supporting Remote Therapeutic Interventions with Mobile Processes 支持移动过程的远程治疗干预

2017 IEEE International Conference on AI & Mobile Services (AIMS) Pub Date : 2017-06-01 DOI: 10.1109/AIMS.2017.13

Marc Schickler, R. Pryss, Johannes Schobel, M. Reichert

{"title":"Supporting Remote Therapeutic Interventions with Mobile Processes","authors":"Marc Schickler, R. Pryss, Johannes Schobel, M. Reichert","doi":"10.1109/AIMS.2017.13","DOIUrl":"https://doi.org/10.1109/AIMS.2017.13","url":null,"abstract":"Many studies have revealed that homework (e.g., relaxation exercises) are crucial for remote therapeutic interventions. In this context, to monitor whether patients actually perform their homework and to check whether they perform it in the right way constitute complex tasks. So far, therapeutic interventions have not been properly supported by IT systems and, hence, the opportunities provided by mobile assistance have been neglected. For example, a smart mobile device may notify a patient about an assigned homework or motivate him to accomplish it in time. Moreover, the patient might be further assisted through a video providing detailed instructions. In turn, the smart mobile device could inform the therapist of the homework outcome. In practice, a proper support of the various types of homework is challenging, even when using modern IT systems. To remedy this drawback, we propose an approach integrating mobile services with process management technology in order to enable the complex coordination tasks that become necessary in connection with homework. For example, a process might enable remote monitoring of homework, giving therapists the opportunity of timely adjustments. In addition, the approach allows involving researchers by providing them with valuable data (e.g., heart rate) gathered during and after homework. This paper presents an approach for creating processes that run on smart mobile devices and enable flexible remote therapeutic intervention support. Such mobile approach significantly enhances therapy assistance on one hand and mobile homework-related scenarios on the other.","PeriodicalId":384523,"journal":{"name":"2017 IEEE International Conference on AI & Mobile Services (AIMS)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122635969","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

ADARM: An Application-Driven Adaptive Resource Management Framework for Data Centers ADARM:数据中心应用驱动的自适应资源管理框架

2017 IEEE International Conference on AI & Mobile Services (AIMS) Pub Date : 2017-06-01 DOI: 10.1109/AIMS.2017.30

Min Luo, Li Li, Wu Chou

引用次数: 6

Towards Psychometrics-Based Friend Recommendations in Social Networking Services 社交网络服务中基于心理测量学的朋友推荐

2017 IEEE International Conference on AI & Mobile Services (AIMS) Pub Date : 2017-05-30 DOI: 10.1109/AIMS.2017.22

Felix Beierle, Kai Grunert, Sebastian Göndör, Viktor Schlüter

引用次数: 10