text:
5}
}

@inproceedings{10.1145/2750858.2807552,
author = {Lee, Min Kyung and Kim, Junsung and Forlizzi, Jodi and Kiesler, Sara},
title = {Personalization Revisited: A Reflective Approach Helps People Better Personalize Health Services and Motivates Them to Increase Physical Activity},
year = {2015},
isbn = {9781450335744},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2750858.2807552},
doi = {10.1145/2750858.2807552},
abstract = {Current approaches to personalization either presuppose people's needs and automatically tailor services or provide formulaic options for people to customize. We propose a complementary approach to personalization: a reflective strategy that helps people realize what matters to them and enables them to better personalize services themselves. To design this strategy, we first studied the practices of eight personal health service providers. We then tested the strategy's efficacy by building a Fitbit Plan website that encouraged Fitbit users to customize a plan or accept an automatically tailored plan. For one group of users, the website used the reflective strategy to assist in the plan setup process. A two-week between-subjects field experiment showed that the reflective strategy helped motivate users to carry out their plans, increasing their average daily steps by 2,425 steps. Without the reflective strategy, users either set easy goals or failed to carry out system-created plans, ultimately showing no change in their average daily steps. This work suggests that helping people reflect on and connect with their own goals in using a personalized service could advance the effectiveness of the service.},
booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {743–754},
numpages = {12},
keywords = {reflective personalization, personal informatics, fitness, activity tracker, health, service, behavior change},
location = {Osaka, Japan},
series = {UbiComp '15}
}

@inproceedings{10.1145/2750858.2805838,
author = {Bedwell, Ben and Slack, Peter and Greenhalgh, Chris},
title = {Learning from the Experts: Enabling and Studying DIY Development of Location-Based Visitor Experiences},
year = {2015},
isbn = {9781450335744},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2750858.2805838},
doi = {10.1145/2750858.2805838},
abstract = {In this paper we show how -- with the aid of enabling technology -- creative Location Based Experiences can be developed for visitors by non-technical professionals from the cultural heritage sector. We look at how these "Place Experts" approach and adopt web technologies to create and publish experiences including the roles they take on, the processes they adopt, and the way they appropriate the technology. We describe our short and long-term research engagements with the cultural heritage sector over the last three years and introduce Wander Anywhere, the website developed to enable this research. We find that place experts typically follow a four stage process in their engagement with location-based experiences, moving from comprehension to translation, development and finally approval. We suggest implications for the processes and technologies that might be employed by others seeking to support a similar type of engagement.},
booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {755–766},
numpages = {12},
keywords = {cultural heritage, location, user studies, location based experience, mobile, authoring},
location = {Osaka, Japan},
series = {UbiComp '15}
}

@inproceedings{10.1145/2750858.2807535,
author = {Jewell, Michael O. and Costanza, Enrico and Kittley-Davies, Jacob},
title = {Connecting the Things to the Internet: An Evaluation of Four Configuration Strategies for Wi-Fi Devices with Minimal User Interfaces},
year = {2015},
isbn = {9781450335744},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2750858.2807535},
doi = {10.1145/2750858.2807535},
abstract = {The availability of low-power Wi-Fi radio modules opens up opportunities to leverage the existing prevalent Wi-Fi infrastructure for large-scale trials and deployments of Ubicomp technology. In this paper we address the challenge of supporting end-users, especially when they are not technical experts, in connecting new low-power, low-cost Wi-Fi devices with very minimal UIs to an existing, secure Wi-Fi infrastructure. We report two usability studies through which 30 participants, with no formal technical training, compared 4 alternative configuration techniques, selected based on cost and consumption constraints, and on adoption in off-the-shelf products. Through an analysis of success rate and causes of failure, our results indicate that two techniques are noticeably more usable than others. These are a web-based configuration mechanism, where users connect to an access point on the Wi-Fi device, and one that makes use of a standard audio cable to connect a smartphone to the device to be configured.},
booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {767–778},
numpages = {12},
keywords = {internet-of-things, deployment, configuration, 802.11, user study},
location = {Osaka, Japan},
series = {UbiComp '15}
}

@inproceedings{10.1145/2750858.2806063,
author = {Woo, Jong-bum and Lim, Youn-kyung},
title = {User Experience in Do-It-Yourself-Style Smart Homes},
year = {2015},
isbn = {9781450335744},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2750858.2806063},
doi = {10.1145/2750858.2806063},
abstract = {Do-it-yourself (DIY)-style smart home products enable users to create their own smart homes by installing sensors and actuators. DIY smart home products are a potential solution to current problems related to home automation products, such as inflexible user controls and high costs of installation. Although the expected user experience of DIY smart home products is different from that of previous home automation products, research on DIY smart home products is still in its early stages. In this paper, we report a 3-week in situ observational study involving eight households. The results suggest six stages of the DIY smart home usage cycle and design implications for improving the user experience of DIY smart home products.},
booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {779–790},
numpages = {12},
keywords = {design, domestic technology, do-it-yourself, end-user programming, smart home},
location = {Osaka, Japan},
series = {UbiComp '15}
}

@inproceedings{10.1145/2750858.2804267,
author = {Narumi, Koya and Hodges, Steve and Kawahara, Yoshihiro},
title = {ConductAR: An Augmented Reality Based Tool for Iterative Design of Conductive Ink Circuits},
year = {2015},
isbn = {9781450335744},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2750858.2804267},
doi = {10.1145/2750858.2804267},
abstract = {Recent advances in materials science have resulted in a range of commercially viable and easy-to-use conductive inks which novices, hobbyists, educators, students and researchers are now using to design and build interactive circuits quickly. Despite the ease with which practitioners can construct working circuits, one of the major limitations of designing circuits on-the-fly is the difficulty of detecting and understanding errors in prototype circuits. As well as short- and open-circuits, which often prevent a circuit from working at all, more subtle issues like high resistance traces can result in poor performance. Many users can't readily work out how to successfully modify their circuits, and they often don't have the tools or expertise to measure the relevant circuit parameters. In this paper we present ConductAR, a tool which can recognize and analyze hand-drawn, printed and hybrid conductive ink patterns. An on-screen augmented reality style interaction helps users to understand and enhance circuit operation. A key element of ConductAR is its ability to calculate the resistance of a circuit using a camera attached to an off-the-shelf PC or tablet. Our sparse coding technique is fast enough to support rapid iterative prototyping on real circuits using a conductive ink marker and/or eraser as shown in Figure 1. The system thereby enhances the feasibility of circuit prototyping with conductive ink.},
booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {791–800},
numpages = {10},
keywords = {conductive ink, rapid prototyping, makers, education},
location = {Osaka, Japan},
series = {UbiComp '15}
}

@inproceedings{10.1145/2750858.2807514,
author = {Turner, Liam D. and Allen, Stuart M. and Whitaker, Roger M.},
title = {Interruptibility Prediction for Ubiquitous Systems: Conventions and New Directions from a Growing Field},
year = {2015},
isbn = {9781450335744},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2750858.2807514},
doi = {10.1145/2750858.2807514},
abstract = {When should a machine attempt to communicate with a user? This is a historical problem that has been studied since the rise of personal computing. More recently, the emergence of pervasive technologies such as the smartphone have extended the problem to be ever-present in our daily lives, opening up new opportunities for context awareness through data collection and reasoning. Complementary to this there has been increasing interest in techniques to intelligently synchronise interruptions with human behaviour and cognition. However, it is increasingly challenging to categorise new developments, which are often scenario specific or scope a problem with particular unique features. In this paper we present a meta-analysis of this area, decomposing and comparing historical and recent works that seek to understand and predict how users will perceive and respond to interruptions. In doing so we identify research gaps, questions and opportunities that characterise this important emerging field for pervasive technology.},
booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {801–812},
numpages = {12},
keywords = {meta-analysis, context-aware computing, interruptibility, ubiquitous computing},
location = {Osaka, Japan},
series = {UbiComp '15}
}

@inproceedings{10.1145/2750858.2807544,
author = {Mehrotra, Abhinav and Musolesi, Mirco and Hendley, Robert and Pejovic, Veljko},
title = {Designing Content-Driven Intelligent Notification Mechanisms for Mobile Applications},
year = {2015},
isbn = {9781450335744},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2750858.2807544},
doi = {10.1145/2750858.2807544},
abstract = {An increasing number of notifications demanding the smartphone user's attention, often arrive at an inappropriate moment, or carry irrelevant content. In this paper we present a study of mobile user interruptibility with respect to notification content, its sender, and the context in which a notification is received. In a real-world study we collect around 70,000 instances of notifications from 35 users. We group notifications according to the applications that initiated them, and the social relationship between the sender and the receiver. Then, by considering both content and context information, such as the current activity of a user, we discuss the design of classifiers for learning the most opportune moment for the delivery of a notification carrying a specific type of information. Our results show that such classifiers lead to a more accurate prediction of users' interruptibility than an alternative approach based on user-defined rules of their own interruptibility.},
booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {813–824},
numpages = {12},
keywords = {mobile sensing, notifications, context-aware computing, interruptibility},
location = {Osaka, Japan},
series = {UbiComp '15}
}

@inproceedings{10.1145/2750858.2804252,
author = {Pielot, Martin and Dingler, Tilman and Pedro, Jose San and Oliver, Nuria},
title = {When Attention is Not Scarce - Detecting Boredom from Mobile Phone Usage},
year = {2015},
isbn = {9781450335744},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2750858.2804252},
doi = {10.1145/2750858.2804252},
abstract = {Boredom is a common human emotion which may lead to an active search for stimulation. People often turn to their mobile phones to seek that stimulation. In this paper, we tackle the challenge of automatically inferring boredom from mobile phone usage. In a two-week in-the-wild study, we collected over 40,000,000 usage logs and 4398 boredom self-reports of 54 mobile phone users. We show that a user-independent machine-learning model of boredom --leveraging features related to recency of communication, usage intensity, time of day, and demographics-- can infer boredom with an accuracy (AUCROC) of up to 82.9%. Results from a second field study with 16 participants suggest that people are more likely to engage with recommended content when they are bored, as inferred by our boredom-detection model. These findings enable boredom-triggered proactive recommender systems that attune their users' level of attention and need for stimulation.},
booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {825–836},
numpages = {12},
keywords = {attention, mobile devices, boredom, killing time, attention economy},
location = {Osaka, Japan},
series = {UbiComp '15}
}

@inproceedings{10.1145/2750858.2807530,
author = {Matic, Aleksandar and Pielot, Martin and Oliver, Nuria},
title = {Boredom-Computer Interaction: Boredom Proneness and the Use of Smartphone},
year = {2015},
isbn = {9781450335744},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2750858.2807530},
doi = {10.1145/2750858.2807530},
abstract = {Mobile technology is becoming a loyal companion in our lives. It is used for increasing amounts of time during the day and night, enabling the development of intelligent user interfaces that characterize their users' traits and adapt to them. In this paper, we show how an individual's tendency to experience boredom, i.e. the personal trait called boredom proneness, affects the use of technology -- specifically a smartphone. We develop machine learning models to automatically classify individuals into high/low boredom proneness from their typical daily patterns of smartphone use. We thus propose boredom proneness as a trait with high potential to enable the design of personalized mobile services that are more meaningful to their users.},
booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {837–841},
numpages = {5},
keywords = {user modeling, mobile devices, boredom proneness},
location = {Osaka, Japan},
series = {UbiComp '15}
}

@inproceedings{10.1145/2750858.2807522,
author = {Murnane, Elizabeth L. and Abdullah, Saeed and Matthews, Mark and Choudhury, Tanzeem and Gay, Geri},
title = {Social (Media) Jet Lag: How Usage of Social Technology Can Modulate and Reflect Circadian Rhythms},
year = {2015},
isbn = {9781450335744},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2750858.2807522},
doi = {10.1145/2750858.2807522},
abstract = {By nature, we are circadian creatures whose bodies' biological clocks drive numerous physiological, mental, and behavioral rhythms. Simultaneously, we are social beings. Accordingly, our internal circadian timings experience interference from externally determined factors such as work schedules and social engagements, and digital connectivity imports additional social constraints that can further misalign our individual body clocks. Misalignment between biological and social time causes social jet lag [50], which has serious physical and mental health consequences. It particularly impacts our sleep processes and neurobehavioral functioning. Examining the interplay between biological rhythms and technology-mediated social interactions, we find that technology may both modulate and reflect circadian rhythms. We also leverage such social-sensor data to infer sleep-related behaviors and disruptions and to analyze variations in attention, cognitive performance, and mood following (in)adequate sleep. We conclude with recommendations for designing technologies attuned to our innate biological traits.},
booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {843–854},
numpages = {12},
keywords = {circadian rhythms, sleep, social computing},
location = {Osaka, Japan},
series = {UbiComp '15}
}

@inproceedings{10.1145/2750858.2804289,
author = {Jayarajah, Kasthuri and Lee, Youngki and Misra, Archan and Balan, Rajesh Krishna},
title = {Need Accurate User Behaviour? Pay Attention to Groups!},
year = {2015},
isbn = {9781450335744},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2750858.2804289},
doi = {10.1145/2750858.2804289},
abstract = {In this paper, we show that characterizing user behaviour from location or smartphone usage traces, without accounting for the interaction of individuals in physical-world groups, can lead to erroneous results. We conducted one of the largest studies in the UbiComp domain thus far, involving indoor location traces of more than 6,000 users, collected over a 4-month period at our university campus, and further studied fine-grained App usage of a subset of 156 Android users. We apply a state-of-the-art group detection algorithm to annotate such location traces with group vs. individual context, and then show that individuals vs. groups exhibit significant differences along three behavioural traits: (1) the mobility pattern, (2) the responsiveness to calls / SMSs and (3) application usage. We show that these significant differences are robust to underlying errors in the group detection technique and that the use of such group context leads to behavioural results that differ from those reported in prior popular work.},
booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {855–866},
numpages = {12},
keywords = {location, app usage, user behaviour, interruptibility, groups},
location = {Osaka, Japan},
series = {UbiComp '15}
}

@inproceedings{10.1145/2750858.2804283,
author = {Ko, Minsam and Choi, Seungwoo and Yang, Subin and Lee, Joonwon and Lee, Uichin},
title = {FamiLync: Facilitating Participatory Parental Mediation of Adolescents' Smartphone Use},
year = {2015},
isbn = {9781450335744},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2750858.2804283},
doi = {10.1145/2750858.2804283},
abstract = {We consider participatory parental mediation in which children engage with their parents in activities that encourage both parents and children to participate in co-learning of digital media use. To this end, we developed FamiLync, a mobile service that treats use-limiting as a family activity and provides the family with a virtual public space to foster social awareness and improve self-regulation. A three-week user study conducted with twelve families in Korea (17 parents and 18 teenagers) showed that FamiLync improves mutual understanding of usage behavior, thereby providing common grounds for parental mediation. Further, parents actively participated in use-limiting with their children, which significantly increased the children's desire to participate. As a consequence, parental mediation methods and parent-child interaction in relation to smartphone usage changed appreciably, and the participants smartphone usage amount significantly decreased.},
booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {867–878},
numpages = {12},
keywords = {adolescents' smartphone overuse, participatory learning, parental mediation},
location = {Osaka, Japan},
series = {UbiComp '15}
}

@inproceedings{10.1145/2750858.2805839,
author = {Yu, Zhiwen and Du, Rong and Guo, Bin and Xu, Huang and Gu, Tao and Wang, Zhu and Zhang, Daqing},
title = {Who Should I Invite for My Party? Combining User Preference and Influence Maximization for Social Events},
year = {2015},
isbn = {9781450335744},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2750858.2805839},
doi = {10.1145/2750858.2805839},
abstract = {The newly emerging event-based social networks (EBSNs) extend social interaction from online to offline, providing an appealing platform for people to organize and participate realworld social events. In this paper, we investigate how to select potential participants in EBSNs from an event host's point of view. We formulate the problem as mining influential and preferable invitee set, considering from two complementary aspects. The first aspect concerns users' preference with respect to the event. The second aspect is influence maximization, which aims to influence the largest number of users to participate the event. In particular, we propose a novel Credit Distribution-User Influence Preference (CD-UIP) algorithm to find the most influential and preferable followers as the invitees. We collect a real-world dataset from a popular EBSNs called "Douban Events", and the experimental results on the dataset demonstrate the proposed algorithm outperforms the state-of-the-art prediction methods.},
booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {879–883},
numpages = {5},
keywords = {invitee set, influence maximization, event-based social networks, user preference, CD-UIP},
location = {Osaka, Japan},
series = {UbiComp '15}
}

@inproceedings{10.1145/2750858.2806061,
author = {Ohara, Kazuya and Maekawa, Takuya and Kishino, Yasue and Shirai, Yoshinari and Naya, Futoshi},
title = {Transferring Positioning Model for Device-Free Passive Indoor Localization},
year = {2015},
isbn = {9781450335744},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2750858.2806061},
doi = {10.1145/2750858.2806061},
abstract = {This paper proposes a new method that makes it easy for us to construct a positioning model for device-free passive indoor localization by using model transfer techniques. With device-free passive indoor positioning, a wireless sensor network is used to detect the movement of a person based on the fact that RF signals transmitted between a transmitter and a receiver are affected by human movement. However, because device-free passive indoor positioning relies on machine learning techniques, we must collect labeled training data at many training points in an end user's environment. This paper proposes a method that transfers a signal strength model used for locating a person obtained in another environment (source environment) to the end user environment. With the transferred models, we can construct a positioning model for the end user environment inexpensively. Our evaluation showed that our method achieved almost the same positioning performance as a supervised method that requires labeled training data obtained in an end user's environment.},
booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {885–896},
numpages = {12},
keywords = {model transfer, device-free passive positioning, indoor positioning},
location = {Osaka, Japan},
series = {UbiComp '15}
}

@inproceedings{10.1145/2750858.2804254,
author = {He, Suining and Chan, S.-H. Gary and Yu, Lei and Liu, Ning},
title = {Calibration-Free Fusion of Step Counter and Wireless Fingerprints for Indoor Localization},
year = {2015},
isbn = {9781450335744},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2750858.2804254},
doi = {10.1145/2750858.2804254},
abstract = {In order to improve the accuracy of fingerprint-based localization, one may fuse step counter measurement with location estimation. Previous works on this often require a pre-calibrating the step counter with training sequence or explicit user input, which is inconvenient for practical deployment. Some assume conditional independence on successive sensor readings, which achieves unsatisfactory accuracy in complex and noisy environment. Some other works need a calibration process for RSSI measurement consistency if different devices are used for offline fingerprint collection and online location query.We propose SLAC, a fingerprint positioning framework which simultaneously localizes the target and calibrates the system. SLAC is calibration-free, and works transparently for heterogeneous devices and users. It is based on a novel formulation embedded with a specialized particle filter, where location estimations, wireless signals and user motion are jointly optimized with resultant consistent and correct model parameters. Extensive experimental trials at HKUST campus and Hong Kong International Airport further confirm that SLAC accommodates device heterogeneity, and achieves significantly lower errors compared with other state-of-the-art algorithms.},
booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {897–908},
numpages = {12},
keywords = {joint optimization, fusion, fingerprinting, indoor localization, device RSSI dependency, step counter calibration},
location = {Osaka, Japan},
series = {UbiComp '15}
}

@inproceedings{10.1145/2750858.2804284,
author = {Biehl, Jacob T. and Lee, Adam J. and Filby, Gerry and Cooper, Matthew},
title = {You're Where? Prove It! Towards Trusted Indoor Location Estimation of Mobile Devices},
year = {2015},
isbn = {9781450335744},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2750858.2804284},
doi = {10.1145/2750858.2804284},
abstract = {Location-enabled applications now permeate the mobile computing landscape. As technologies like Bluetooth Low Energy (BLE) and Apple's iBeacon protocols begin to see widespread adoption, we will no doubt see a proliferation of indoor location enabled application experiences. While not essential to each of these applications, many will require that the location of the device be true and verifiable. In this paper, we present LocAssure, a new framework for trusted indoor location estimation. The system leverages existing technologies like BLE and iBeacons, making the solution practical and compatible with technologies that are already in use today. In this work, we describe our system, situate it within a broad location assurance taxonomy, describe the protocols that enable trusted localization in our system, and provide an analysis of early deployment and use characteristics. Through developer APIs, LocAssure can provide critical security support for a broad range of indoor location applications.},
booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {909–919},
numpages = {11},
keywords = {location services, secure location, indoor location, location-based access control},
location = {Osaka, Japan},
series = {UbiComp '15}
}

@inproceedings{10.1145/2750858.2807543,
author = {Bao, Xuan and Liu, Bin and Tang, Bo and Hu, Bing and Kong, Deguang and Jin, Hongxia},
title = {PinPlace: Associate Semantic Meanings with Indoor Locations without Active Fingerprinting},
year = {2015},
isbn = {9781450335744},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2750858.2807543},
doi = {10.1145/2750858.2807543},
abstract = {Web map services today, such as Google and Bing maps, have digitalized a great portion of the physical world into easily accessible location databases. After the industry invested huge efforts in gathering related information, a user now can search a physical location on the map and know what kind of place it is, known as reverse geo-coding. However, this functionality is mostly limited to public outdoor locations and to building level granularity. We believe that many services can benefit from knowing the semantic meanings of fine-grained locations including indoor places. For example, the phone can mute and delay incoming calls when a user enters a meeting room. Cameras can be disabled in bathrooms to protect users' privacy. In this paper, we present PinPlace, an on-device service that can automatically associate semantic meanings with outdoor and indoor locations using the activity, transit, and time related features.},
booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {921–925},
numpages = {5},
location = {Osaka, Japan},
series = {UbiComp '15}
}

@inproceedings{10.1145/2750858.2804286,
author = {Shan, Zhangqing and Wu, Hao and Sun, Weiwei and Zheng, Baihua},
title = {COBWEB: A Robust Map Update System Using GPS Trajectories},
year = {2015},
isbn = {9781450335744},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2750858.2804286},
doi = {10.1145/2750858.2804286},
abstract = {The accuracy and completeness of a digital map plays a critical role in determining the quality of most location-based services. Unfortunately, road networks change frequently. Consequently, we study the issue of automatic map update in this paper. We propose a system called COBWEB which takes all the unmatched trajectories as input and generates the missing road segments with both the geometry properties and topology features well preserved. We conduct a comprehensive experimental study via real trajectory data generated by roughly 15,000 taxis in Singapore within a 5-month period. Compared with existing work, COBWEB demonstrates a better and more stable performance and a stronger resilience to various sampling rates and data sizes.},
booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {927–937},
numpages = {11},
keywords = {map update, map inference, GPS noise, GPS trajectories, map matching, low sampling rate},
location = {Osaka, Japan},
series = {UbiComp '15}
}

@inproceedings{10.1145/2750858.2804272,
author = {Lee, Seungwoo and Kim, Yungeun and Ahn, Daye and Ha, Rhan and Lee, Kyoungwoo and Cha, Hojung},
title = {Non-Obstructive Room-Level Locating System in Home Environments Using Activity Fingerprints from Smartwatch},
year = {2015},
isbn = {9781450335744},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2750858.2804272},
doi = {10.1145/2750858.2804272},
abstract = {Many smart home applications, such as monitoring for the elderly and home automation, require location information for individual occupants. Several techniques have been proposed for tracking occupants in a home environment. However, the current techniques do not provide a seamless in-home locating system owing to the occupants' device-free movement and the lack of cost-effective infrastructure for home location tracking. In this paper, we propose a home occupant tracking system that uses a smartphone and an off-the-shelf smartwatch without additional infrastructure. In our system, activity fingerprints are automatically generated from the microphone and the inertial sensors of the smartwatch, and location information is periodically obtained from the smartphone. We designed a hidden Markov model using the relationship between home activities and the room's location. Extensive experiments showed that our system tracks the location of users with 87% accuracy, even when there is no manual training for activities.},
booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {939–950},
numpages = {12},
keywords = {machine learning, context-aware computing, in-home locating system, occupant tracking, mobile sensing},
location = {Osaka, Japan},
series = {UbiComp '15}
}

@inproceedings{10.1145/2750858.2804275,
author = {Wang, Shuai and Yu, Xiaofeng and Xie, Junqing},
title = {WISDOM: An Efficient Framework of Predicting WLAN Availability with Cellular Fingerprints},
year = {2015},
isbn = {9781450335744},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2750858.2804275},
doi = {10.1145/2750858.2804275},
abstract = {Mobile devices with both WLAN adapter and cellular capability, which are also known as dual-mode mobile terminals, are facing various challenges and problems in conventional WLAN discovery mechanisms, including inefficiency in network discovery, unavoidable energy consumption for frequent WLAN scanning, and privacy information leaking in network probing. In this paper, we propose a novel framework called WISDOM (Wireless Indicator Supervised Data Offloading Manipulation), which can efficiently predict the availability of appropriate WLAN access points (APs) for mobile device without the need of turning on its WLAN adapter in advance. WISDOM takes advantage of historical cellular fingerprints (i.e., the pairs of Cell-ID and Received Signal Strength Indicator) to directly model the WLAN coverage, and perform WLAN availability prediction based on the models given a query cellular fingerprint. Similarity and Classification methods are introduced to work in the framework as prediction methods. We have developed a WISDOM prototype and performed simulation and real field tests under various situations. The results showed WISDOM along with the proposed predication methods could reach at least an average of 80% in accuracy and saving 60% of power consumption on average for mobile devices.},
booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {951–962},
numpages = {12},
keywords = {cellular fingerprints, machine learning, WLAN availability prediction},
location = {Osaka, Japan},
series = {UbiComp '15}
}

@inproceedings{10.1145/2750858.2807516,
author = {Xu, Han and Yang, Zheng and Zhou, Zimu and Shangguan, Longfei and Yi, Ke and Liu, Yunhao},
title = {Enhancing Wifi-Based Localization with Visual Clues},
year = {2015},
isbn = {9781450335744},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2750858.2807516},
doi = {10.1145/2750858.2807516},
abstract = {Indoor localization is of great importance to a wide range of applications in the era of mobile computing. Current mainstream solutions rely on Received Signal Strength (RSS) of wireless signals as fingerprints to distinguish and infer locations. However, those methods suffer from fingerprint ambiguity that roots in multipath fading and temporal dynamics of wireless signals. Though pioneer efforts have resorted to motion-assisted or peer-assisted localization, they neither work in real time nor work without the help of peer users, which introduces extra costs and constraints, and thus degrades their practicality. To get over these limitations, we propose Argus, an image-assisted localization system for mobile devices. The basic idea of Argus is to extract geometric constraints from crowdsourced photos, and to reduce fingerprint ambiguity by mapping the constraints jointly against the fingerprint space. We devise techniques for photo selection, geometric constraint extraction, joint location estimation, and build a prototype that runs on commodity phones. Extensive experiments show that Argus triples the localization accuracy of classic RSS-based method, in time no longer than normal WiFi scanning, with negligible energy consumption.},
booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {963–974},
numpages = {12},
keywords = {photogrammetry, smart phone, indoor localization},
location = {Osaka, Japan},
series = {UbiComp '15}
}

@inproceedings{10.1145/2750858.2807538,
author = {Kleiminger, Wilhelm and Beckel, Christian and Santini, Silvia},
title = {Household Occupancy Monitoring Using Electricity Meters},
year = {2015},
isbn = {9781450335744},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2750858.2807538},
doi = {10.1145/2750858.2807538},
abstract = {Occupancy monitoring (i.e. sensing whether a building or room is currently occupied) is required by many building automation systems. An automatic heating system may, for example, use occupancy data to regulate the indoor temperature. Occupancy data is often obtained through dedicated hardware such as passive infrared sensors and magnetic reed switches. In this paper, we derive occupancy information from electric load curves measured by off-the-shelf smart electricity meters. Using the publicly available ECO dataset, we show that supervised machine learning algorithms can extract occupancy information with an accuracy between 83% and 94%. To this end we use a comprehensive feature set containing 35 features. Thereby we found that the inclusion of features that capture changes in the activation state of appliances provides the best occupancy detection accuracy.},
booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {975–986},
numpages = {12},
keywords = {smart meter, opportunistic sensing, electricity consumption, occupancy detection},
location = {Osaka, Japan},
series = {UbiComp '15}
}

@inproceedings{10.1145/2750858.2807525,
author = {Basu, Chandrayee and Koehler, Christian and Das, Kamalika and Dey, Anind K.},
title = {PerCCS: Person-Count from Carbon Dioxide Using Sparse Non-Negative Matrix Factorization},
year = {2015},
isbn = {9781450335744},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2750858.2807525},
doi = {10.1145/2750858.2807525},
abstract = {Occupancy count in rooms is valuable for applications such as room utilization, opportunistic meeting support, and efficient heating-cooling operations. Few buildings, however, have the means of knowing occupancy beyond simple binary presence-absence. In this paper we present the PerCCS algorithm that explores the possibility of estimating person count from CO2 sensors already integrated in everyday room air-conditioning infrastructure. PerCSS uses task-driven Sparse Non-negative Matrix Factorization (SNMF) to learn a nonnegative low-dimensional representation of the CO2 data in the preprocessing stage. This denoised CO2 acts as the predictor variable for estimating occupancy count using Ensemble Least Square Regression. We tested the algorithm to estimate 15 minutes average occupancy count from a classroom of capacity 42 and compared its performance against existing methods from the literature. PerCSS estimates occupancy with a normalized mean squared error (NMSE) of 0.075 and outperformed our comparative methods in predicting occupancy count with 91 % and 15 % for exact occupancy estimation, when the room was unoccupied and occupied respectively, whereas the competing methods failed mostly.},
booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {987–998},
numpages = {12},
keywords = {machine learning, building energy efficiency},
location = {Osaka, Japan},
series = {UbiComp '15}
}

@inproceedings{10.1145/2750858.2806897,
author = {Saleheen, Nazir and Ali, Amin Ahsan and Hossain, Syed Monowar and Sarker, Hillol and Chatterjee, Soujanya and Marlin, Benjamin and Ertin, Emre and al'Absi, Mustafa and Kumar, Santosh},
title = {PuffMarker: A Multi-Sensor Approach for Pinpointing the Timing of First Lapse in Smoking Cessation},
year = {2015},
isbn = {9781450335744},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2750858.2806897},
doi = {10.1145/2750858.2806897},
abstract = {Recent researches have demonstrated the feasibility of detecting smoking from wearable sensors, but their performance on real-life smoking lapse detection is unknown. In this paper, we propose a new model and evaluate its performance on 61 newly abstinent smokers for detecting a first lapse. We use two wearable sensors --- breathing pattern from respiration and arm movements from 6-axis inertial sensors worn on wrists. In 10-fold cross-validation on 40 hours of training data from 6 daily smokers, our model achieves a recall rate of 96.9%, for a false positive rate of 1.1%. When our model is applied to 3 days of post-quit data from 32 lapsers, it correctly pinpoints the timing of first lapse in 28 participants. Only 2 false episodes are detected on 20 abstinent days of these participants. When tested on 84 abstinent days from 28 abstainers, the false episode per day is limited to 1/6.},
booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {999–1010},
numpages = {12},
keywords = {smoking detection, smoking cessation, wearable sensors, smartwatch, mobile health (mHealth)},
location = {Osaka, Japan},
series = {UbiComp '15}
}

@inproceedings{10.1145/2750858.2805844,
author = {Suarez, Isabel and Jahn, Andreas and Anderson, Christoph and David, Klaus},
title = {Improved Activity Recognition by Using Enriched Acceleration Data},
year = {2015},
isbn = {9781450335744},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2750858.2805844},
doi = {10.1145/2750858.2805844},
abstract = {Sensors embedded in smartphones are an essential component for activity recognition. Even though the accelerometer is the most widely used sensor, the highest recognition accuracies are obtained when using data collected from multiple sensors. However, the use of multiple sensors has an adverse impact on the energy consumption of power-limited devices such as smartphones. In this paper, we present a new method to improve the recognition accuracy of physical activities by using only the accelerometer. We utilize a low-pass filter to split the acceleration data into a low- and a high-frequency component. These components provide a new set of features, which can be used as a complement to the raw acceleration to reduce the number of sensors needed to recognize physical activities. After evaluating our method for a public dataset, we found that our approach represents an average of up to 16% increase in the recognition accuracy over the raw acceleration data, outperforming even widely used combinations such as the raw acceleration plus the gyroscope. The highest accuracies are obtained when using a cut-off frequency in the interval [0:001--0:05] Hz as well as a combination of the acceleration with its low-frequency component.},
booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {1011–1015},
numpages = {5},
keywords = {low-pass filter, activity recognition, acceleration},
location = {Osaka, Japan},
series = {UbiComp '15}
}

@inproceedings{10.1145/2750858.2806064,
author = {Sleeper, Manya and Schnorf, Sebastian and Kemler, Brian and Consolvo, Sunny},
title = {Attitudes toward Vehicle-Based Sensing and Recording},
year = {2015},
isbn = {9781450335744},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2750858.2806064},
doi = {10.1145/2750858.2806064},
abstract = {Vehicles increasingly include features that rely on hi-tech sensors and recording; however, little is known of public attitudes toward such recording. We use two studies, an online survey (n=349) and an interview-based study (n=15), to examine perceptions of vehicle-based sensing and recording. We focus on: 1) how vehicle-based recording and sensing may differ from perceptions of current recording; 2) factors that impact comfort with vehicle-based recording for hypothetical drivers versus bystanders; and 3) perceptions of potential privacy-preserving techniques. We find that vehicle-based recording challenges current mental models of recording awareness. Comfort tends to depend on perceived benefits, which can vary by stakeholder type. Perceived privacy in spaces near cars can also impact comfort and reflect mental models of private spaces as well as the range of potentially sensitive activities people perform in and near cars. Privacy-preserving techniques may increase perceived comfort but may require addressing trust and usability issues.},
booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {1017–1028},
numpages = {12},
keywords = {privacy, vehicles, perceived benefits, privacy-preserving mechanisms, sensors, cars, attitudes, recording, cameras},
location = {Osaka, Japan},
series = {UbiComp '15}
}

@inproceedings{10.1145/2750858.2807545,
author = {Thomaz, Edison and Essa, Irfan and Abowd, Gregory D.},
title = {A Practical Approach for Recognizing Eating Moments with Wrist-Mounted Inertial Sensing},
year = {2015},
isbn = {9781450335744},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2750858.2807545},
doi = {10.1145/2750858.2807545},
abstract = {Recognizing when eating activities take place is one of the key challenges in automated food intake monitoring. Despite progress over the years, most proposed approaches have been largely impractical for everyday usage, requiring multiple on-body sensors or specialized devices such as neck collars for swallow detection. In this paper, we describe the implementation and evaluation of an approach for inferring eating moments based on 3-axis accelerometry collected with a popular off-the-shelf smartwatch. Trained with data collected in a semi-controlled laboratory setting with 20 subjects, our system recognized eating moments in two free-living condition studies (7 participants, 1 day; 1 participant, 31 days), with F-scores of 76.1% (66.7% Precision, 88.8% Recall), and 71.3% (65.2% Precision, 78.6% Recall). This work represents a contribution towards the implementation of a practical, automated system for everyday food intake monitoring, with applicability in areas ranging from health research and food journaling.},
booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {1029–1040},
numpages = {12},
keywords = {dietary intake, activity recognition, automated dietary assessment, inertial sensors, food journaling},
location = {Osaka, Japan},
series = {UbiComp '15}
}

@inproceedings{10.1145/2750858.2807551,
author = {Hammerla, Nils Y. and Pl\"{o}tz, Thomas},
title = {Let's (Not) Stick Together: Pairwise Similarity Biases Cross-Validation in Activity Recognition},
year = {2015},
isbn = {9781450335744},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2750858.2807551},
doi = {10.1145/2750858.2807551},
abstract = {The ability to generalise towards either new users or unforeseen behaviours is a key requirement for activity recognition systems in ubiquitous computing. Differences in recognition performance for the two application cases can be significant, and user-dependent performance is typically assumed to be an upper bound on performance. We demonstrate that this assumption does not hold for the widely used cross-validation evaluation scheme that is typically employed both during system bootstrapping and for reporting results. We describe how the characteristics of segmented time-series data render random cross-validation a poor fit, as adjacent segments are not statistically independent. We develop an alternative approach -- meta-segmented cross validation -- that explicitly circumvents this issue and evaluate it on two data-sets. Results indicate a significant drop in performance across a variety of feature extraction and classification methods if this bias is removed, and that prolonged, repetitive activities are particularly affected.},
booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {1041–1051},
numpages = {11},
keywords = {cross validation, evaluation, activity recognition, model selection},
location = {Osaka, Japan},
series = {UbiComp '15}
}

@inproceedings{10.1145/2750858.2804256,
author = {Nguyen, Le T. and Zeng, Ming and Tague, Patrick and Zhang, Joy},
title = {I Did Not Smoke 100 Cigarettes Today! Avoiding False Positives in Real-World Activity Recognition},
year = {2015},
isbn = {9781450335744},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2750858.2804256},
doi = {10.1145/2750858.2804256},
abstract = {Activity recognition (AR) systems are typically built and evaluated on a predefined set of activities. AR systems work best if the test data contains and only contains these predefined activities. In real world applications, AR systems trained in this manner generate serious false positives, for example if "smoking" is one of the activities in the training data but "lifting weights" is not. Due to the similarity of two activities, an AR system may report a user smoking 100 times a day but he actually did a bicep workout 100 times. In this work, we propose a new approach to train an AR system leveraging the large quantity of unlabeled data which reflects activities users perform in real life. The proposed mPUL (Multi-class Positive and Unlabeled Learning) approach significantly reduces the false positives. We argue that mPUL is a much more effective training method for real-world AR applications.},
booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {1053–1063},
numpages = {11},
keywords = {semi-supervised learning, open-world, multi-class positive and unlabeled learning, activity recognition},
location = {Osaka, Japan},
series = {UbiComp '15}
}

@inproceedings{10.1145/2750858.2807546,
author = {Bourgeois, Jacky and Foell, Stefan and Kortuem, Gerd and Price, Blaine A. and van der Linden, Janet and Elbanhawy, Eiman Y. and Rimmer, Christopher},
title = {Harvesting Green Miles from My Roof: An Investigation into Self-Sufficient Mobility with Electric Vehicles},
year = {2015},
isbn = {9781450335744},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2750858.2807546},
doi = {10.1145/2750858.2807546},
abstract = {Electric vehicles are an increasingly attractive option for households to reduce carbon emissions, especially when they are powered by renewable energy. In this paper we report the results of an 18-month field trial investigating the desirability and feasibility of powering electric vehicles (EVs) with domestic solar electricity. Based on extensive collection of data from 7 households including over 75,000 miles of daily EV use, home electricity consumption and generation, and in-depth interviews with householders we develop a detailed understanding of what drives EV decisions in households, quantify to what extent our participating households currently power their EVs with solar electricity, and investigate how feasible the vision of "self-sustaining electric mobility" is. We use this understanding to draw implications for future research into supporting emerging practices of EV drivers.},
booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {1065–1076},
numpages = {12},
keywords = {sustainability, data mining, electric vehicle, solar electricity, microgeneration, user study, domestic charging},
location = {Osaka, Japan},
series = {UbiComp '15}
}

@inproceedings{10.1145/2750858.2804274,
author = {Brush, A. J. Bernheim and Krumm, John and Gupta, Sidhant and Patel, Shwetak},
title = {EVHomeShifter: Evaluating Intelligent Techniques for Using Electrical Vehicle Batteries to Shift When Homes Draw Energy from the Grid},
year = {2015},
isbn = {9781450335744},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2750858.2804274},
doi = {10.1145/2750858.2804274},
abstract = {Time of use tiered pricing schedules encourage shifting electricity demand from peak to off-peak hours. Charging times for electric vehicles (EV) can be shifted into overnight hours, which are usually off-peak. EVs can also be used as energy storage devices, available during certain peak hours to power a house with electricity stored during off-peak hours. Studies suggest both techniques are practical, but were based on simulated demand patterns or large commercial fleets. To investigate feasibility on a per home basis, we collected data from 15 EV homes using the Lab of Things sensing infrastructure. We evaluate a scheme that powers homes with their car battery during expensive electricity periods and then charges the battery during cheaper periods. We show an average potential savings of $10.91/month for shifting charging times, and an additional $13.58/month for powering the home from the EV, even accounting for the inefficiencies of electric conversion.},
booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {1077–1088},
numpages = {12},
keywords = {load leveling, home energy use, sustainability, residential, sensing, lab of things, electric vehicles},
location = {Osaka, Japan},
series = {UbiComp '15}
}

@inproceedings{10.1145/2750858.2805831,
author = {Huang, Chuan-Che (Jeff) and Yang, Rayoung and Newman, Mark W.},
title = {The Potential and Challenges of Inferring Thermal Comfort at Home Using Commodity Sensors},
year = {2015},
isbn = {9781450335744},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2750858.2805831},
doi = {10.1145/2750858.2805831},
abstract = {For decades, researchers have investigated ways to infer human thermal comfort. Studies have usually required cumbersome sensors and human observers, making them inappropriate for use in naturalistic settings such as the home. Emerging wearable and smart home sensing devices offer the opportunity to develop new models of thermal comfort based on data collected in-situ. To explore this opportunity, we deployed a sensing system in seven homes and collected self-report data from 11 participants for four weeks. Our system captures many factors employed in previous thermal comfort research, as well as new factors (e.g., activity level, sweat level). Machine learning-based models derived from the collected data show improvement over previous techniques, however significant prediction errors remain. In analyzing these errors we identify six problems that pose challenges for inferring comfort in the wild. Based on our findings, we suggest techniques to improve future in-situ thermal comfort modeling efforts.},
booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {1089–1100},
numpages = {12},
keywords = {smart home, thermal comfort, wearable sensors},
location = {Osaka, Japan},
series = {UbiComp '15}
}

@inproceedings{10.1145/2750858.2805835,
author = {Nakamura, Shoko and Shigaki, Saeko and Hiromori, Akihito and Yamaguchi, Hirozumi and Higashino, Teruo},
title = {A Model-Based Approach to Support Smart and Social Home Living},
year = {2015},
isbn = {9781450335744},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2750858.2805835},
doi = {10.1145/2750858.2805835},
abstract = {A system to improve the quality of human life is developed and proposed. A model-based approach is used where smart home residents, appliances, energy sources and correlations among them are comprehensively modeled. The model was integrated with activity recognition information that enables the system to suggest smart life tips that provide advice to residents in a non-intrusive way. A crowd-sourced large-scale survey of 1,000 subjects was conducted that enabled important tips for improving the quality of human life to be quantified. On the basis of the survey results, quantitative metrics and strategies were designed for presenting suitable tips in a timely manner depending on the lifestyle of subjects. The system was evaluated by (1) 34 actual subjects in virtual smart homes and (2) family members in an actual house in an experiment lasting more than one month in which actual sensors were deployed.},
booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {1101–1105},
numpages = {5},
keywords = {crowdsourcing, smart home, smart life support},
location = {Osaka, Japan},
series = {UbiComp '15}
}

@inproceedings{10.1145/2750858.2805833,
author = {Wang, Haoyu and Hong, Jason and Guo, Yao},
title = {Using Text Mining to Infer the Purpose of Permission Use in Mobile Apps},
year = {2015},
isbn = {9781450335744},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2750858.2805833},
doi = {10.1145/2750858.2805833},
abstract = {Understanding the purpose of why sensitive data is used could help improve privacy as well as enable new kinds of access control. In this paper, we introduce a new technique for inferring the purpose of sensitive data usage in the context of Android smartphone apps. We extract multiple kinds of features from decompiled code, focusing on app-specific features and text-based features. These features are then used to train a machine learning classifier. We have evaluated our approach in the context of two sensitive permissions, namely ACCESS_FINE_LOCATION and READ_CONTACT_LIST, and achieved an accuracy of about 85% and 94% respectively in inferring purposes. We have also found that text-based features alone are highly effective in inferring purposes.},
booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {1107–1118},
numpages = {12},
keywords = {permission, purpose, mobile applications, privacy, Android},
location = {Osaka, Japan},
series = {UbiComp '15}
}

@inproceedings{10.1145/2750858.2805827,
author = {Higuchi, Takamasa and Martin, Paul and Chakraborty, Supriyo and Srivastava, Mani},
title = {AnonyCast: Privacy-Preserving Location Distribution for Anonymous Crowd Tracking Systems},
year = {2015},
isbn = {9781450335744},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2750858.2805827},
doi = {10.1145/2750858.2805827},
abstract = {Fusion of infrastructure-based pedestrian tracking systems and embedded sensors on mobile devices holds promise for providing accurate positioning in large public buildings. However, privacy concerns regarding handling of sensitive user location data potentially disrupt the adoption of such systems. This paper presents AnonyCast, a novel privacy-aware mechanism for delivering precise location information measured by crowd-tracking systems to individual pedestrians' smartphones. AnonyCast uses sparsely placed Bluetooth Low Energy transmitters to advertise location-dependent, time-varying keys. Using location measurements, AnonyCast estimates a subset of keys that each pedestrian's phone receives along its path. By combining a cryptography scheme called CP-ABE with a novel greedy algorithm for key selection, it encrypts each path before publishing, allowing users to decrypt only their own trajectories. The results from field experiments show that AnonyCast delivers accurate locations over 84% of time, bounding probability of unauthorized access to one's location below 1%.},
booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {1119–1130},
numpages = {12},
keywords = {crowd tracking, location privacy, trajectory identification, ciphertext-policy attribute-based encryption},
location = {Osaka, Japan},
series = {UbiComp '15}
}

@inproceedings{10.1145/2750858.2804281,
author = {Guha, Shion and Wicker, Stephen B.},
title = {Spatial Subterfuge: An Experience Sampling Study to Predict Deceptive Location Disclosures},
year = {2015},
isbn = {9781450335744},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2750858.2804281},
doi = {10.1145/2750858.2804281},
abstract = {Prior research shows that people often engage in deception when sharing location. Privacy concerns, social surveillance and impression management are the primary drivers of these types of behaviors. One methodological question that arises in this research context is the problem of reliable measurement to study predictors of deceptive location disclosure from usage data. In this note, we propose a simple experience sampling method (ESM) approach that is useful for studying this phenomenon. We describe our ESM deployment and report the results of a long term, quantitative study of 204 foursquare users over 1 year. Results indicate that physical distance, tie strength and order of visibility on the foursquare feed are significant predictors (with moderate to high effect sizes) of deceptive location disclosure. We connect these findings to the rich tradition of location disclosure behavior research in ubiquitous computing.},
booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {1131–1135},
numpages = {5},
keywords = {ESM, foursquare, privacy, impressions, deception, visibility},
location = {Osaka, Japan},
series = {UbiComp '15}
}

@inproceedings{10.1145/2750858.2805836,
author = {Naghizade, Elham and Bailey, James and Kulik, Lars and Tanin, Egemen},
title = {How Private Can i Be among Public Users?},
year = {2015},
isbn = {9781450335744},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2750858.2805836},
doi = {10.1145/2750858.2805836},
abstract = {People are increasingly volunteering personal data. Services based on this data rely on a high number of participants and high data quality. Personal data is often seen as private and individuals are more likely to provide such data if they can choose its granularity, e.g., instead of an exact value, they may provide a range. Focusing on spatial crowdsourced data, this work aims to determine whether the common method of coarsening location data of privacy-conscious individuals is an effective approach if fine-grained location data has also been submitted by privacy-apathetic users. We propose a novel inference attack to refine the location of privacy-conscious individuals. Our experiments suggest that even with a dataset that is mostly populated with privacy-conscious users, our technique succeeds with high precision and recall.},
booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {1137–1141},
numpages = {5},
keywords = {matrix factorization, crowdsourced trajectory database, privacy},
location = {Osaka, Japan},
series = {UbiComp '15}
}

@inproceedings{10.1145/2750858.2806060,
author = {Chollet, Mathieu and W\"{o}rtwein, Torsten and Morency, Louis-Philippe and Shapiro, Ari and Scherer, Stefan},
title = {Exploring Feedback Strategies to Improve Public Speaking: An Interactive Virtual Audience Framework},
year = {2015},
isbn = {9781450335744},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2750858.2806060},
doi = {10.1145/2750858.2806060},
abstract = {Good public speaking skills convey strong and effective communication, which is critical in many professions and used in everyday life. The ability to speak publicly requires a lot of training and practice. Recent technological developments enable new approaches for public speaking training that allow users to practice in a safe and engaging environment. We explore feedback strategies for public speaking training that are based on an interactive virtual audience paradigm. We investigate three study conditions: (1) a non-interactive virtual audience (control condition), (2) direct visual feedback, and (3) nonverbal feedback from an interactive virtual audience. We perform a threefold evaluation based on self-assessment questionnaires, expert assessments, and two objectively annotated measures of eye-contact and avoidance of pause fillers. Our experiments show that the interactive virtual audience brings together the best of both worlds: increased engagement and challenge as well as improved public speaking skills as judged by experts.},
booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {1143–1154},
numpages = {12},
keywords = {virtual reality, public speaking, multimodal interfaces, training},
location = {Osaka, Japan},
series = {UbiComp '15}
}

@inproceedings{10.1145/2750858.2807534,
author = {Khan, Aftab and Mellor, Sebastian and Berlin, Eugen and Thompson, Robin and McNaney, Roisin and Olivier, Patrick and Pl\"{o}tz, Thomas},
title = {Beyond Activity Recognition: Skill Assessment from Accelerometer Data},
year = {2015},
isbn = {9781450335744},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2750858.2807534},
doi = {10.1145/2750858.2807534},
abstract = {The next generation of human activity recognition applications in ubiquitous computing scenarios focuses on assessing the quality of activities, which goes beyond mere identification of activities of interest. Objective quality assessments are often difficult to achieve, hard to quantify, and typically require domain specific background information that bias the overall judgement and limit generalisation. In this paper we propose a framework for skill assessment in activity recognition that enables automatic quality analysis of human activities. Our approach is based on a hierarchical rule induction technique that effectively abstracts from noise-prone activity data and assesses activity data at different temporal contexts. Our approach requires minimal domain specific knowledge about the activities of interest, which makes it largely generalisable. By means of an extensive case study we demonstrate the effectiveness of the proposed framework in the context of dexterity training of 15 medical students engaging in 50 attempts of surgical activities.},
booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {1155–1166},
numpages = {12},
keywords = {classification, rule induction, accelerometer, activity recognition, skill assessment},
location = {Osaka, Japan},
series = {UbiComp '15}
}

@inproceedings{10.1145/2750858.2804265,
author = {Fung, Michelle and Jin, Yina and Zhao, RuJie and Hoque, Mohammed (Ehsan)},
title = {ROC Speak: Semi-Automated Personalized Feedback on Nonverbal Behavior from Recorded Videos},
year = {2015},
isbn = {9781450335744},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2750858.2804265},
doi = {10.1145/2750858.2804265},
abstract = {We present a framework that couples computer algorithms with human intelligence in order to automatically sense and interpret nonverbal behavior. The framework is cloud-enabled and ubiquitously available via a web browser, and has been validated in the context of public speaking. The system automatically captures audio and video data in-browser through the user's webcam, and then analyzes the data for smiles, movement, and volume modulation. Our framework allows users to opt in and receive subjective feedback from Mechanical Turk workers ("Turkers"). Our system synthesizes the Turkers' interpretations, ratings, and comment rankings with the machine-sensed data and enables users to interact with, explore, and visualize personalized and presentational feedback. Our results provide quantitative and qualitative evidence in support of our proposed synthesized feedback, relative to video-only playback with impersonal tips. Our interface can be seen here: http://tinyurl.com/feedback-ui (Supported in Google Chrome.)},
booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {1167–1178},
numpages = {12},
keywords = {automated feedback, nonverbal behavior interpretation, public speaking, automated multimodal affect sensing},
location = {Osaka, Japan},
series = {UbiComp '15}
}

@inproceedings{10.1145/2750858.2805829,
author = {Ryan, Cynthia and Ciesinski, Katherine and Hoque, Mohammed (Ehsan)},
title = {Vowel Shapes: An Open-Source, Interactive Tool to Assist Singers with Learning Vowels},
year = {2015},
isbn = {9781450335744},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2750858.2805829},
doi = {10.1145/2750858.2805829},
abstract = {The mastery of vowel production is central to developing vocal technique and may be influenced by language, musical context or a coach's direction. Currently, students learn through verbal descriptions, demonstration of correct vowel sounds, and customized exercises. Vowel Shapes is an interactive practice tool that automatically captures and visualizes vowel sounds in real time to assist singers in correctly producing target vowels. The system may be used during a lesson or as a practice tool when an instructor is not present. Our system's design was informed by iterative evaluations with 14 students and their vocal professor from the Eastman School of Music, University of Rochester. Results from an exploratory evaluation of the system with 10 students indicated that 70% of the participants improved their time to reach an instructor-defined target. 90% of the students in the evaluation would use this system during practice sessions.},
booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {1179–1183},
numpages = {5},
keywords = {singing, vowels, visualization, interdisciplinary design, auditory I/O and sound in the UI, interactive},
location = {Osaka, Japan},
series = {UbiComp '15}
}

@inproceedings{10.1145/2750858.2807550,
author = {Singh, Indrajeet and Krishnamurthy, Srikanth V. and Madhyastha, Harsha V. and Neamtiu, Iulian},
title = {ZapDroid: Managing Infrequently Used Applications on Smartphones},
year = {2015},
isbn = {9781450335744},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2750858.2807550},
doi = {10.1145/2750858.2807550},
abstract = {User surveys have shown that a typical user has over a hundred apps on her smartphone [1], but stops using many of them. We conduct a user study to identify such unused apps, which we call zombies, and show via experiments that zombie apps consume significant resources on a user's smartphone and access her private information. We then design and build ZapDroid, which enables users to detect and silo zombie apps in an effective way to prevent their undesired activities. If and when the user wishes to resume using such an app, ZapDroid restores the app quickly and effectively. Our evaluations show that: (i) ZapDroid saves twice the energy from unwanted zombie app behaviors as compared to apps from the Play Store that kill background unwanted processes, and (ii) it effectively prevents zombie apps from using undesired permissions. In addition, ZapDroid is energ-efficient, consuming < 4% of the battery per day.},
booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {1185–1196},
numpages = {12},
keywords = {energy, smartphones, privacy},
location = {Osaka, Japan},
series = {UbiComp '15}
}

@inproceedings{10.1145/2750858.2807542,
author = {Jones, Simon L. and Ferreira, Denzil and Hosio, Simo and Goncalves, Jorge and Kostakos, Vassilis},
title = {Revisitation Analysis of Smartphone App Use},
year = {2015},
isbn = {9781450335744},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2750858.2807542},
doi = {10.1145/2750858.2807542},
abstract = {We present a revisitation analysis of smartphone use to investigate the question: do smartphones induce usage habits? We analysed three months of application launch logs from 165 users in naturalistic settings. Our analysis reveals distinct clusters of applications and users which share similar revisitation patterns. However, we show that much of smartphone usage on a macro-level is very similar to web browsing on desktops, and thus argue that smartphone usage is driven by innate service needs rather than technology characteristics. On the other hand, on a micro-level we identify unique characteristics in smartphone usage, and we present a rudimentary model that accounts for 92% in the variability of our smartphone use.},
booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {1197–1208},
numpages = {12},
keywords = {habits, revisitation, user behaviour, smartphone use},
location = {Osaka, Japan},
series = {UbiComp '15}
}

@inproceedings{10.1145/2750858.2804292,
author = {Mathur, Arunesh and Schlotfeldt, Brent and Chetty, Marshini},
title = {A Mixed-Methods Study of Mobile Users' Data Usage Practices in South Africa},
year = {2015},
isbn = {9781450335744},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2750858.2804292},
doi = {10.1145/2750858.2804292},
abstract = {With a shift towards usage-based billing, the questions of how data costs affect mobile Internet use and how users manage mobile data arise. In this paper, we describe a mixed-methods study of mobile phone users' data usage practices in South Africa, a country where usage-based billing is prevalent and where data costs are high, to answer these questions. We do so using a large scale survey, in-depth interviews, and logs of actual data usage over time. Our findings suggest that unlike in more developed settings, when data is limited or expensive, mobile Internet users are extremely cost-conscious, and employ various strategies to optimize mobile data usage such as actively disconnecting from the mobile Internet to save data. Based on these findings, we suggest how the Ubicomp and related research communities can better support users that need to carefully manage their data to optimize costs.},
booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {1209–1220},
numpages = {12},
keywords = {mobile data costs, mobile data tracking, mobile data},
location = {Osaka, Japan},
series = {UbiComp '15}
}

@inproceedings{10.1145/2750858.2805837,
author = {Jesdabodi, Chakajkla and Maalej, Walid},
title = {Understanding Usage States on Mobile Devices},
year = {2015},
isbn = {9781450335744},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2750858.2805837},
doi = {10.1145/2750858.2805837},
abstract = {Nowadays, mobile apps are used for nearly every situation: for planning the day, communicating with colleagues, ordering goods, or entertaining and socializing. To understand users expectations in each situation and to provide context-aware services, researchers and app vendors started to capture users' interaction with the smartphone and to model user's behavior. This paper reports on a behavioral study based on app usage data logged over one year and the corresponding apps descriptions from the app store. Using Topic Modeling and clustering techniques, we segmented the usage data into meaningful clusters that correspond to different "states", in which users normally use their smartphone, e.g. socializing or consuming media. Researchers and app-vendors can use the insights from our work to improve their contextual recommendation techniques and the overall usage experience.},
booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {1221–1225},
numpages = {5},
keywords = {behavioral profiles, apps, usage data, intent identification},
location = {Osaka, Japan},
series = {UbiComp '15}
}

@inproceedings{10.1145/2750858.2807532,
author = {Ackad, Christopher and Clayphan, Andrew and Tomitsch, Martin and Kay, Judy},
title = {An In-the-Wild Study of Learning Mid-Air Gestures to Browse Hierarchical Information at a Large Interactive Public Display},
year = {2015},
isbn = {9781450335744},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2750858.2807532},
doi = {10.1145/2750858.2807532},
abstract = {This paper describes the design and evaluation of our Media Ribbon, a large public interactive display for browsing hierarchical information, with mid-air gestures. Browsing a hierarchical information space is a fundamental form of interaction. Designing learnable mid-air gestures is a current challenge for large display interaction. Our in-the-wild evaluation draws on 41 days of quantitative log data, with 4484 gestures detected, and qualitative data from 15 interviews, and associated video. We explored: whether our design enabled people to learn the gestures; how our tutorial and feedback mechanisms supported learning; and the effectiveness of support for browsing hierarchical information. Our contributions are: (1) design of large public display for browsing of hierarchical information; (2) with its gesture set; (3) insights into the ways people learn and use this interface in our context; and (4) guidelines for designing learnable mid-air gestures.},
booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {1227–1238},
numpages = {12},
keywords = {user centred design and pervasive computing, gestural interaction, interactive public information displays},
location = {Osaka, Japan},
series = {UbiComp '15}
}

@inproceedings{10.1145/2750858.2805842,
author = {Cheung, Victor and Scott, Stacey D.},
title = {A Laboratory-Based Study Methodology to Investigate Attraction Power of Large Public Interactive Displays},
year = {2015},
isbn = {9781450335744},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2750858.2805842},
doi = {10.1145/2750858.2805842},
abstract = {A known challenge of designing large public interactive displays is to create an interface that attracts a passerby's attention and communicates its interactivity. However, typical "in-the-wild" field study methods of assessing public display design solutions require costly system implementation and deployment, creating challenges for assessing early stage design concepts. Such studies also limit the amount of experimental control researchers have over the environment, limiting the precision of results. To address these issues, we developed a complementary laboratory-based study methodology that employs experimental deception to assess the ability of an interface design solution to attract a passerby's attention. Our methodology enables more rigorous control of confounding factors, study of early-stage prototypes, and requires minimal setup. We used this methodology to assess existing visual design solutions for drawing attention and enticing interaction, compare our results to previous studies, and reflect on the benefits and limitations of this assessment approach.},
booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {1239–1250},
numpages = {12},
keywords = {large interactive displays, public space, experimental design, laboratory study},
location = {Osaka, Japan},
series = {UbiComp '15}
}

@inproceedings{10.1145/2750858.2807518,
author = {Williamson, Julie R. and Sund\'{e}n, Daniel and Bradley, Jay},
title = {GlobalFestival: Evaluating Real World Interaction on a Spherical Display},
year = {2015},
isbn = {9781450335744},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2750858.2807518},
doi = {10.1145/2750858.2807518},
abstract = {Spherical displays present compelling opportunities for interaction in public spaces. However, there is little research into how touch interaction should control a spherical surface or how these displays are used in real world settings. This paper presents an in the wild deployment of an application for a spherical display called GlobalFestival that utilises two different touch interaction techniques. The first version of the application allows users to spin and tilt content on the display, while the second version only allows spinning the content. During the 4-day deployment, we collected overhead video data and on-display interaction logs. The analysis brings together quantitative and qualitative methods to understand how users approach and move around the display, how on screen interaction compares in the two versions of the application, and how the display supports social interaction given its novel form factor.},
booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {1251–1261},
numpages = {11},
keywords = {spherical displays, human computer interaction, multi-touch interaction, in the wild evaluation},
location = {Osaka, Japan},
series = {UbiComp '15}
}

@inproceedings{10.1145/2750858.2804255,
author = {Walter, Robert and Bulling, Andreas and Lindlbauer, David and Schuessler, Martin and M\"{u}ller, J\"{o}rg},
title = {Analyzing Visual Attention during Whole Body Interaction with Public Displays},
year = {2015},
isbn = {9781450335744},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2750858.2804255},
doi = {10.1145/2750858.2804255},
abstract = {While whole body interaction can enrich user experience on public displays, it remains unclear how common visualizations of user representations impact users' ability to perceive content on the display. In this work we use a head-mounted eye tracker to record visual behavior of 25 users interacting with a public display game that uses a silhouette user representation, mirroring the users' movements. Results from visual attention analysis as well as post-hoc recall and recognition tasks on display contents reveal that visual attention is mostly on users' silhouette while peripheral screen elements remain largely unattended. In our experiment, content attached to the user representation attracted significantly more attention than other screen contents, while content placed at the top and bottom of the screen attracted significantly less. Screen contents attached to the user representation were also significantly better remembered than those at the top and bottom of the screen.},
booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {1263–1267},
numpages = {5},
keywords = {public displays, mobile eye tracking, visual attention, user representation, whole body interaction},
location = {Osaka, Japan},
series = {UbiComp '15}
}

@inproceedings{10.1145/2750858.2807533,
author = {Inoue, Sozo and Ueda, Naonori and Nohara, Yasunobu and Nakashima, Naoki},
title = {Mobile Activity Recognition for a Whole Day: Recognizing Real Nursing Activities with Big Dataset},
year = {2015},
isbn = {9781450335744},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2750858.2807533},
doi = {10.1145/2750858.2807533},
abstract = {In this paper, we provide a real nursing data set for mobile activity recognition that can be used for supervised machine learning, and big data combined the patient medical records and sensors attempted for 2 years, and also propose a method for recognizing activities for a whole day utilizing prior knowledge about the activity segments in a day. Furthermore, we demonstrate data mining by applying our method to the bigger data with additional hospital data. In the proposed method, we 1) convert a set of segment timestamps into a prior probability of the activity segment by exploiting the concept of importance sampling, 2) obtain the likelihood of traditional recognition methods for each local time window within the segment range, and, 3) apply Bayesian estimation by marginalizing the conditional probability of estimating the activities for the segment samples. By evaluating with the dataset, the proposed method outperformed the traditional method without using the prior knowledge by 25.81% at maximum by balanced classification rate. Moreover, the proposed method significantly reduces duration errors of activity segments from 324.2 seconds of the traditional method to 74.6 seconds at maximum. We also demonstrate the data mining by applying our method to bigger data in a hospital.},
booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {1269–1280},
numpages = {12},
keywords = {mobile activity recognition, dataset, domain-specific activity recognition, nursing activity},
location = {Osaka, Japan},
series = {UbiComp '15}
}

@inproceedings{10.1145/2750858.2805828,
author = {Zhang, Fuzheng and Yuan, Nicholas Jing and Zheng, Kai and Lian, Defu and Xie, Xing and Rui, Yong},
title = {Mining Consumer Impulsivity from Offline and Online Behavior},
year = {2015},
isbn = {9781450335744},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2750858.2805828},
doi = {10.1145/2750858.2805828},
abstract = {Consumer impulsivity is a psychological feature characterizing the impulsive buying tendency. In this paper, by bridging consumer behavior with perceived stimuli on social networks, we present a computational framework, termed Consumer Impulsivity Model (CIM), for exploring a consumer's impulsivity in both offline and online context: consumption-related location visit indicating consumption patterns in the physical realm, and online shopping behavior indicating economic activities on the Internet. To demonstrate the effectiveness of CIM, we conduct extensive experiments, with a large dataset we have collected from thousands of consumers. The results show that 1) for 103 subjects, the inferred consumer impulsivity has a positive Pearson correlation with survey results in the situation of product and product category, respectively. 2) females inferred impulsivity is higher than males on average in the situation of product and product category, respectively. Age has a negative Pearson correlation with inferred impulsivity in the situation of POI, POI category and product category, respectively. 3) for next behavior prediction, our model defeats several presented baselines. These results suggest that our framework CIM offers a powerful paradigm for 1) presenting an effective measurement for consumer impulsivity. 2) uncovering the correlation between consumer impulsivity and demographic factors and 3) revealing that the introduction of impulsivity is effective in predicting consumer behavior.},
booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {1281–1292},
numpages = {12},
keywords = {consumer impulsivity, online shopping, check-in},
location = {Osaka, Japan},
series = {UbiComp '15}
}

@inproceedings{10.1145/2750858.2805845,
author = {Canzian, Luca and Musolesi, Mirco},
title = {Trajectories of Depression: Unobtrusive Monitoring of Depressive States by Means of Smartphone Mobility Traces Analysis},
year = {2015},
isbn = {9781450335744},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2750858.2805845},
doi = {10.1145/2750858.2805845},
abstract = {One of the most interesting applications of mobile sensing is monitoring of individual behavior, especially in the area of mental health care. Most existing systems require an interaction with the device, for example they may require the user to input his/her mood state at regular intervals. In this paper we seek to answer whether mobile phones can be used to unobtrusively monitor individuals affected by depressive mood disorders by analyzing only their mobility patterns from GPS traces. In order to get ground-truth measurements, we have developed a smartphone application that periodically collects the locations of the users and the answers to daily questionnaires that quantify their depressive mood. We demonstrate that there exists a significant correlation between mobility trace characteristics and the depressive moods. Finally, we present the design of models that are able to successfully predict changes in the depressive mood of individuals by analyzing their movements.},
booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {1293–1304},
numpages = {12},
keywords = {spatial statistics, GPS traces, depression, mobile sensing},
location = {Osaka, Japan},
series = {UbiComp '15}
}

@inproceedings{10.1145/2750858.2804290,
author = {Gouveia, R\'{u}ben and Karapanos, Evangelos and Hassenzahl, Marc},
title = {How Do We Engage with Activity Trackers? A Longitudinal Study of Habito},
year = {2015},
isbn = {9781450335744},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2750858.2804290},
doi = {10.1145/2750858.2804290},
abstract = {We report on a 10-month in-the-wild study of the adoption, engagement and discontinuation of an activity tracker called Habito, by a sample of 256 users who installed the tracker on their own volition. We found 'readiness' to behavior change to be a strong predictor of adoption (which ranged from 56% to 20%). Among adopters, only a third updated their daily goal, which in turn impacted their physical activity levels. The use of the tracker was dominated by glances -- brief, 5-sec sessions where users called the app to check their current activity levels with no further interaction, while users displayed true lack of interest in historical data. Textual feedback proved highly effective in fueling further engagement with the tracker as well as inducing physical activity. We propose three directions for design: designing for different levels of 'readiness', designing for multilayered and playful goal setting, and designing for sustained engagement.},
booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {1305–1316},
numpages = {12},
keywords = {personal informatics, persuasive technologies, behavior change technologies, physical activity trackers},
location = {Osaka, Japan},
series = {UbiComp '15}
}

license_type: permissive

branch_name: refs/heads/main

extension: bib


text:
//go:generate ava-enum -f=$GOFILE --marshal --lower

package brokerService

// BrokerServiceType x ENUM(
// HTTP
// Kafka
// Memory
// NATS
// RabbitMQ
// Unknown
// )
type BrokerServiceType int32

branch_name: refs/heads/develop

gha_license_id: MIT

gha_language: Go

extension: go