395 research outputs found
Enhancing Mobile App User Understanding and Marketing with Heterogeneous Crowdsourced Data: A Review
Ā© 2013 IEEE. The mobile app market has been surging in recent years. It has some key differentiating characteristics which make it different from traditional markets. To enhance mobile app development and marketing, it is important to study the key research challenges such as app user profiling, usage pattern understanding, popularity prediction, requirement and feedback mining, and so on. This paper reviews CrowdApp, a research field that leverages heterogeneous crowdsourced data for mobile app user understanding and marketing. We first characterize the opportunities of the CrowdApp, and then present the key research challenges and state-of-the-art techniques to deal with these challenges. We further discuss the open issues and future trends of the CrowdApp. Finally, an evolvable app ecosystem architecture based on heterogeneous crowdsourced data is presented
Identifying Hidden Visits from Sparse Call Detail Record Data
Despite a large body of literature on trip inference using call detail record
(CDR) data, a fundamental understanding of their limitations is lacking. In
particular, because of the sparse nature of CDR data, users may travel to a
location without being revealed in the data, which we refer to as a "hidden
visit". The existence of hidden visits hinders our ability to extract reliable
information about human mobility and travel behavior from CDR data. In this
study, we propose a data fusion approach to obtain labeled data for statistical
inference of hidden visits. In the absence of complementary data, this can be
accomplished by extracting labeled observations from more granular cellular
data access records, and extracting features from voice call and text messaging
records. The proposed approach is demonstrated using a real-world CDR dataset
of 3 million users from a large Chinese city. Logistic regression, support
vector machine, random forest, and gradient boosting are used to infer whether
a hidden visit exists during a displacement observed from CDR data. The test
results show significant improvement over the naive no-hidden-visit rule, which
is an implicit assumption adopted by most existing studies. Based on the
proposed model, we estimate that over 10% of the displacements extracted from
CDR data involve hidden visits. The proposed data fusion method offers a
systematic statistical approach to inferring individual mobility patterns based
on telecommunication records
Automated Medical Device Display Reading Using Deep Learning Object Detection
Telemedicine and mobile health applications, especially during the quarantine
imposed by the covid-19 pandemic, led to an increase on the need of
transferring health monitor readings from patients to specialists. Considering
that most home medical devices use seven-segment displays, an automatic display
reading algorithm should provide a more reliable tool for remote health care.
This work proposes an end-to-end method for detection and reading seven-segment
displays from medical devices based on deep learning object detection models.
Two state of the art model families, EfficientDet and EfficientDet-lite,
previously trained with the MS-COCO dataset, were fine-tuned on a dataset
comprised by medical devices photos taken with mobile digital cameras, to
simulate real case applications. Evaluation of the trained model show high
efficiency, where all models achieved more than 98% of detection precision and
more than 98% classification accuracy, with model EfficientDet-lite1 showing
100% detection precision and 100% correct digit classification for a test set
of 104 images and 438 digits.Comment: 6 pages, 5 figure
Towards Proactive Context-aware Computing and Systems
A primary goal of context-aware systems is delivering the right information at the
right place and right time to users in order to enable them to make effective decisions and improve their quality of life. There are three key requirements for achieving this goal:
determining what information is relevant, personalizing it based on the usersā context (location, preferences, behavioral history etc.), and delivering it to them in a timely manner without an explicit request from them. These requirements create a paradigm that we term as āProactive Context-aware Computingā.
Most of the existing context-aware systems fulfill only a subset of these requirements.
Many of these systems focus only on personalization of the requested information
based on usersā current context. Moreover, they are often designed for specific domains.
In addition, most of the existing systems are reactive - the users request for some information and the system delivers it to them. These systems are not proactive i.e. they cannot anticipate usersā intent and behavior and act proactively without an explicit request from them. In order to overcome these limitations, we need to conduct a deeper analysis and enhance our understanding of context-aware systems that are generic, universal, proactive and applicable to a wide variety of domains.
To support this dissertation, we explore several directions. Clearly the most significant
sources of information about users today are smartphones. A large amount of usersā context can be acquired through them and they can be used as an effective means
to deliver information to users. In addition, social media such as Facebook, Flickr and
Foursquare provide a rich and powerful platform to mine usersā interests, preferences and behavioral history. We employ the ubiquity of smartphones and the wealth of information available from social media to address the challenge of building proactive context-aware systems. We have implemented and evaluated a few approaches, including some as part of the Rover framework, to achieve the paradigm of Proactive Context-aware Computing. Rover is a context-aware research platform which has been evolving for the last 6 years.
Since location is one of the most important context for users, we have developed
āLocusā, an indoor localization, tracking and navigation system for multi-story buildings.
Other important dimensions of usersā context include the activities that they are engaged
in. To this end, we have developed āSenseMeā, a system that leverages the smartphone and its multiple sensors in order to perform multidimensional context and activity recognition for users. As part of the āSenseMeā project, we also conducted an exploratory study of privacy, trust, risks and other concerns of users with smart phone based personal sensing systems and applications.
To determine what information would be relevant to usersā situations, we have developed āTellMeā - a system that employs a new, flexible and scalable approach based on Natural Language Processing techniques to perform bootstrapped discovery and ranking of relevant information in context-aware systems. In order to personalize the relevant information, we have also developed an algorithm and system for mining a broad range of usersā preferences from their social network profiles and activities. For recommending new information to the users based on their past behavior and context history (such as visited locations, activities and time), we have developed a recommender system and approach for performing multi-dimensional collaborative recommendations using tensor factorization.
For timely delivery of personalized and relevant information, it is essential to anticipate
and predict usersā behavior. To this end, we have developed a unified infrastructure,
within the Rover framework, and implemented several novel approaches and algorithms
that employ various contextual features and state of the art machine learning techniques
for building diverse behavioral models of users. Examples of generated models include
classifying usersā semantic places and mobility states, predicting their availability for accepting calls on smartphones and inferring their device charging behavior. Finally, to
enable proactivity in context-aware systems, we have also developed a planning framework based on HTN planning. Together, these works provide a major push in the direction of proactive context-aware computing
- ā¦