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Abstract
User intent is an integral part of mobile phone applications as it delivers events to applications, notifies applications of relevant events, or triggers applications. Current smartphone applications either require users to manually start them or they run as background jobs. In this work, we propose UserIntent, a new paradigm for automatically selecting the right smartphone application based on user intent captured. UserIntent consist of two parts: user intent detection and mechanism for triggering a smartphone app. Action cues act as user intent and a context-aware selection algorithm chooses a suitable smartphone application. In order to demonstrate how UserIntent works, we also develop a concrete application that recognizes speaker and talk content based on gestures captured.
In this paper, we propose SocioPhone, a novel initiative to build a mobile platform for face-to-face interaction monitoring. Face-to-face interaction, especially conversation, is a fundamental part of everyday life. Interaction-aware applications aimed at facilitating group conversations have been proposed, but have not proliferated yet. Useful contexts to capture and support face-to-face interactions need to be explored more deeply. More important, recognizing delicate conversational contexts with commodity mobile devices requires solving a number of technical challenges. As a first step to address such challenges, we identify useful meta-linguistic contexts of conversation, such as turn-takings, prosodic features, a dominant participant, and pace. These serve as cornerstones for building a variety of interaction-aware applications. SocioPhone abstracts such useful meta-linguistic contexts as a set of intuitive APIs. Its runtime efficiently monitors registered contexts during in-progress conversations and notifies applications on-the-fly. Importantly, we have noticed that online turn monitoring is the basic building block for extracting diverse meta-linguistic contexts, and have devised a novel volume-topography-based method. We show the usefulness of SocioPhone with several interesting applications: SocioTherapist, SocioDigest, and Tug-of-War. Also, we show that our turn-monitoring technique is highly accurate and energy-efficient under diverse real-life situations.
Mobile devices are becoming ubiquitous. People use their phones as a personal concierge discovering and making decisions anywhere and anytime. Understanding user intent on the go therefore becomes important for task completion on the phone. While existing efforts have predominantly focused on understanding the explicit user intent expressed by a textual or voice query, this paper presents an approach to context-aware and personalized entity recommendation which understands the implicit intent without any explicit user input on the phone. The approach, highly motivated from a large-scale mobile click-through analysis, is able to rank both the entity types and the entities within each type (here an entity is a local business, e.g., "I love sushi," while an entity type is a category, e.g., "restaurant"). The recommended entity types and entities are relevant to both user context (past behaviors) and sensor context (time and geo-location). Specifically, it estimates the generation probability of an entity by a given user conditioned on the current context in a probabilistic framework. A random-walk propagation is then employed to refine the estimated probability by mining the temporal patterns among entities. We deploy a recommendation application based on the proposed approach on Window Phone 7 devices. We evaluate recommendation performance on 10 thousand mobile clicks, as well as user experience through subjective user studies. We show that the application is effective to facilitate the exploration and discovery of surroundings for mobile users.
A noise map facilitates monitoring of environmental noise pollution in urban areas. It can raise citizen awareness of noise pollution levels, and aid in the development of mitigation strategies to cope with the adverse effects. However, state-of-the-art techniques for rendering noise maps in urban areas are expensive and rarely updated (months or even years), as they rely on population and traffic models rather than on real data. Participatory urban sensing can be leveraged to create an open and inexpensive platform for rendering up-to-date noise maps. In this paper, we present the design, implementation and performance evaluation of an end-to-end participatory urban noise mapping system called Ear-Phone. Ear-Phone, for the first time, leverages Compressive Sensing to address the fundamental problem of recovering the noise map from incomplete and random samples obtained by crowdsourcing data collection. Ear-Phone, implemented on Nokia N95 and HP iPAQ mobile devices, also addresses the challenge of collecting accurate noise pollution readings at a mobile device. Extensive simulations and outdoor experiments demonstrate that Ear-Phone is a feasible platform to assess noise pollution, incurring reasonable system resource consumption at mobile devices and providing high reconstruction accuracy of the noise map.
Recent evidence suggests that mobile search is becoming an in- creasingly important way for mobile users to gain access to on- line information, especially as off-portal content continues to grow rapidly. In this paper we study the characteristics of mobile search by analysing approximately 6 million individual search requests generated by over 260,000 individual mobile searchers over a 7- day period during 2006. We analyse the patterns of queries used by mobile searchers and focus on key characteristics such as the click- thru rates of mobile searches in order to understand, for the first time, just how well mobile search engines are responding to user queries. Moreover, we compare our results to a number of recent mobile studies and highlight some of the key differences between mobile search and traditional Web search behaviours.
We consider the problem of monitoring road and traffic conditions in a city. Prior work in this area has required the deployment of dedicated sensors on vehicles and/or on the roadside, or the tracking of mobile phones by service providers. Furthermore, prior work has largely focused on the developed world, with its relatively simple traffic flow patterns. In fact, traffic flow in cities of the developing regions, which comprise much of the world, tends to be much more complex owing to varied road conditions (e.g., potholed roads), chaotic traffic (e.g., a lot of braking and honking), and a heterogeneous mix of vehicles (2-wheelers, 3-wheelers, cars, buses, etc.). To monitor road and traffic conditions in such a setting, we present Nericell, a system that performs rich sensing by piggybacking on smartphones that users carry around with them. In this demo, we show the use of accelerometer to detect bumps and braking. We also use the microphone to enable honk detection. Nericell addresses several challenges including virtually reorienting the accelerometer on a phone that is at an arbitrary orientation, and performing honk detection and localization in an energy efficient manner.
We present the design, implementation, evaluation, and user experiences of the CenceMe application, which represents the first system that combines the inference of the pres- ence of individuals using off-the-shelf, sensor-enabled mobile phones with sharing of this information through social net- working applications such as Facebook and MySpace. We discuss the system challenges for the development of soft- ware on the Nokia N95 mobile phone. We present the de- sign and tradeoffs of split-level classification, whereby per- sonal sensing presence (e.g., walking, in conversation, at the gym) is derived from classifiers which execute in part on the phones and in part on the backend servers to achieve scal- able inference. We report performance measurements that characterize the computational requirements of the software and the energy consumption of the CenceMe phone client. We validate the system through a user study where twenty two people, including undergraduates, graduates and fac- ulty, used CenceMe continuously over a three week period in a campus town. From this user study we learn how the system performs in a production environment and what uses people find for a personal sensing system.
Today's mobile phones represent a rich and powerful computing platform, given their sensing, processing and communication capabilities. Phones are also part of the everyday life of billions of people, and therefore represent an exceptionally suitable tool for conducting social and psychological experiments in an unobtrusive way. de the ability of sensing individual emotions as well as activities, verbal and proximity interactions among members of social groups. Moreover, the system is programmable by means of a declarative language that can be used to express adaptive rules to improve power saving. We evaluate a system prototype on Nokia Symbian phones by means of several small-scale experiments aimed at testing performance in terms of accuracy and power consumption. Finally, we present the results of real deployment where we study participants emotions and interactions. We cross-validate our measurements with the results obtained through questionnaires filled by the users, and the results presented in social psychological studies using traditional methods. In particular, we show how speakers and participants' emotions can be automatically detected by means of classifiers running locally on off-the-shelf mobile phones, and how speaking and interactions can be correlated with activity and location measures.