Xiaojuan Ma

TripPlanner: Personalized Trip Planning Leveraging Heterogeneous Crowdsourced Digital Footprints

Planning an itinerary before traveling to a city is one of the most important travel preparation activities. In this paper, we propose a novel framework called TripPlanner, leveraging a combination of location-based social network (i.e., LBSN) and taxi GPS digital footprints to achieve personalized, interactive, and traffic-aware trip planning. First, we construct a dynamic point-of-interest network model by extracting relevant information from crowdsourced LBSN and taxi GPS traces. Then, we propose a two-phase approach for personalized trip planning. In the route search phase, TripPlanner works interactively with users to generate candidate routes with specified venues. In the route augmentation phase, TripPlanner applies heuristic algorithms to add users preferred venues iteratively to the candidate routes, with the objective of maximizing the route score while satisfying both the venue visiting time and total travel time constraints. To validate the efficiency and effectiveness of the proposed approach, extensive empirical studies were performed on two real-world data sets from the city of San Francisco, which contain more than 391 900 passenger delivery trips generated by 536 taxis in a month and 110 214 check-ins left by 15 680 Foursquare users in six months.

MobiGroup: Enabling Lifecycle Support to Social Activity Organization and Suggestion With Mobile Crowd Sensing

This paper presents a group-aware mobile crowd sensing system called MobiGroup, which supports group activity organization in real-world settings. Acknowledging the complexity and diversity of group activities, this paper introduces a formal concept model to characterize group activities and classifies them into four organizational stages. We then present an intelligent approach to support group activity preparation, including a heuristic rule-based mechanism for advertising public activity and a context-based method for private group formation. In addition, we leverage features extracted from both online and offline communities to recommend ongoing events to attendees with different needs. Compared with the baseline method, people preferred public activities suggested by our heuristic rule-based method. Using a dataset collected from 45 participants, we found that the context-based approach for private group formation can attain a precision and recall of over 80%, and the usage of spatial-temporal contexts and group computing can have more than a 30% performance improvement over considering the interaction frequency between a user and related groups. A case study revealed that, by extracting the features such as dynamic intimacy and static intimacy, our cross-community approach for ongoing event recommendation can meet different user needs.

crowddeliver : Planning City-Wide Package Delivery Paths Leveraging the Crowd of Taxis

Despite the great demand on and attempts at package express shipping services, online retailers have not yet had a practical solution to make such services profitable. In this paper, we propose an economical approach to express package delivery, i.e., exploiting relays of taxis with passengers to help transport package collectively, without degrading the quality of passenger services. Specifically, we propose a two-phase framework called crowddeliver for the package delivery path planning. In the first phase, we mine the historical taxi trajectory data offline to identify the shortest package delivery paths with estimated travel time given any Origin–Destination pairs. Using the paths and travel time as the reference, in the second phase we develop an online adaptive taxi scheduling algorithm to find the near-optimal delivery paths iteratively upon real-time requests and direct the package routing accordingly. Finally, we evaluate the two-phase framework using the real-world data sets, which consist of a point of interest, a road network, and the large-scale trajectory data, respectively, that are generated by 7614 taxis in a month in the city of Hangzhou, China. Results show that over 85% of packages can be delivered within 8 hours, with around 4.2 relays of taxis on average.

Speaking through pictures: images vs. icons

People with aphasia, a condition that impairs the ability to understand or generate written or spoken language, are aided by assistive technology that helps them communicate through a vocabulary of icons. These systems are akin to language translation systems, translating icon arrangements into spoken or written language and vice versa. However, these icon-based systems have little vocabulary breadth or depth, making it difficult for people with aphasia to apply their usage to multiple real world situations. Pictures from the web are numerous, varied, and easily accessible and thus, could potentially address the small size issues of icon-based systems. We present results from two studies that investigate this potential and demonstrate that images can be as effective as icons when used as a replacement for English language communication. The first study uses elderly subjects to investigate the efficacy of images vs. icons in conveying word meaning; the second study examines the retention of word-level meaning by both images and icons with a population of aphasics. We conclude that images collected from the web are as functional as icons in conveying information and thus, are feasible to use in assistive technology that supports people with aphasia.

How well do visual verbs work in daily communication for young and old adults

In this paper we study how verbs are visually conveyed in daily communication contexts for both young and old adults. Four visual modes are compared: a single static image, a panel of four static images, an animation, and a video clip. The results reveal age effects, as well as performance differences introduced by lexical verb properties and visual cues. We also suggest guidelines for visual verb creation.

A Generic Framework for Constraint-Driven Data Selection in Mobile Crowd Photographing

Mobile crowd photographing (MCP) is an emerging area of interest for researchers as the built-in cameras of mobile devices are becoming one of the commonly used visual logging approaches in our daily lives. In order to meet diverse MCP application requirements and constraints of sensing targets, a multifacet task model should be defined for a generic MCP data collection framework. Furthermore, MCP collects pictures in a distributed way in which a large number of contributors upload pictures whenever and wherever it is suitable. This inevitably leads to evolving picture streams. This paper investigates the multiconstraint-driven data selection problem in MCP picture aggregation and proposes a pyramid-tree (PTree) model which can efficiently select an optimal subset from the evolving picture streams based on varied coverage needs of MCP tasks. By utilizing the PTree model in a generic MCP data collection framework, which is called CrowdPic, we test and evaluate the effectiveness, efficiency, and flexibility of the proposed framework through crowdsourcing-based and simulation-based experiments. Both the theoretical analysis and simulation results indicate that the PTree-based framework can effectively select a subset with high utility coverage and low redundancy ratio from the streaming data. The overall framework is also proved flexible and applicable to a wide range of MCP task scenarios.

Developing a Comprehensive Engagement Framework of Gamification for Reflective Learning

Engagement is a key reason for introducing gamification to learning and thus serves as an important measurement of its effectiveness. Based on a literature review and meta-synthesis, this paper proposes a comprehensive framework of engagement in gamification for learning. The framework sketches out the connections among gamification strategies, dimensions of engagement, and the ultimate learning outcome. It also elicits other task - and user - related factors that may potentially impact the effect of gamification on learner engagement. To verify and further strengthen the framework, we conducted a user study to demonstrate that: 1) different gamification strategies can trigger different facets of engagement; 2) the three dimensions of engagement have varying effects on skill acquisition and transfer; and 3) task nature and learner characteristics that were overlooked in previous studies can influence the engagement process. Our framework provides an in-depth understanding of the mechanism of gamification for learning, and can serve as a theoretical foundation for future research and design.

Dynamic cluster-based over-demand prediction in bike sharing systems

Bike sharing is booming globally as a green transportation mode, but the occurrence of over-demand stations that have no bikes or docks available greatly affects user experiences. Directly predicting individual over-demand stations to carry out preventive measures is difficult, since the bike usage pattern of a station is highly dynamic and context dependent. In addition, the fact that bike usage pattern is affected not only by common contextual factors (e.g., time and weather) but also by opportunistic contextual factors (e.g., social and traffic events) poses a great challenge. To address these issues, we propose a dynamic cluster-based framework for over-demand prediction. Depending on the context, we construct a weighted correlation network to model the relationship among bike stations, and dynamically group neighboring stations with similar bike usage patterns into clusters. We then adopt Monte Carlo simulation to predict the over-demand probability of each cluster. Evaluation results using real-world data from New York City and Washington, D.C. show that our framework accurately predicts over-demand clusters and outperforms the baseline methods significantly.

Vocabulary navigation made easier

It is challenging to search a dictionary consisting of thousands of entries in order to select appropriate words for building written communication. This is true both for people trying to communicate in a foreign language who have not developed a full vocabulary, for school children learning to write, for authors who wish to be more precise and expressive, and especially for people with lexical access disorders. We make vocabulary navigation and word finding easier by augmenting a basic vocabulary with links between words based on human judgments of semantic similarity. In this paper, we report the results from a user study evaluating how our system named ViVA performs compared to a widely used assistive vocabulary in which words are organized hierarchically into common categories.

SoundNet: investigating a language composed of environmental sounds

Auditory displays have been used in both human-machine and computer interfaces. However, the use of non-speech audio in assistive communication for people with language disabilities, or in other applications that employ visual representations, is still under-investigated. In this paper, we introduce SoundNet, a linguistic database that associates natural environmental sounds with words and concepts. A sound labeling study was carried out to verify SoundNet associations and to investigate how well the sounds evoke concepts. A second study was conducted using the verified SoundNet data to explore the power of environmental sounds to convey concepts in sentence contexts, compared with conventional icons and animations. Our results show that sounds can effectively illustrate (especially concrete) concepts and can be applied to assistive interfaces.