8,570 research outputs found
Visual analytics of location-based social networks for decision support
Recent advances in technology have enabled people to add location information to social networks called Location-Based Social Networks (LBSNs) where people share their communication and whereabouts not only in their daily lives, but also during abnormal situations, such as crisis events. However, since the volume of the data exceeds the boundaries of human analytical capabilities, it is almost impossible to perform a straightforward qualitative analysis of the data. The emerging field of visual analytics has been introduced to tackle such challenges by integrating the approaches from statistical data analysis and human computer interaction into highly interactive visual environments. Based on the idea of visual analytics, this research contributes the techniques of knowledge discovery in social media data for providing comprehensive situational awareness. We extract valuable hidden information from the huge volume of unstructured social media data and model the extracted information for visualizing meaningful information along with user-centered interactive interfaces. We develop visual analytics techniques and systems for spatial decision support through coupling modeling of spatiotemporal social media data, with scalable and interactive visual environments. These systems allow analysts to detect and examine abnormal events within social media data by integrating automated analytical techniques and visual methods. We provide comprehensive analysis of public behavior response in disaster events through exploring and examining the spatial and temporal distribution of LBSNs. We also propose a trajectory-based visual analytics of LBSNs for anomalous human movement analysis during crises by incorporating a novel classification technique. Finally, we introduce a visual analytics approach for forecasting the overall flow of human crowds
An Empirical Study on Android for Saving Non-shared Data on Public Storage
With millions of apps that can be downloaded from official or third-party
market, Android has become one of the most popular mobile platforms today.
These apps help people in all kinds of ways and thus have access to lots of
user's data that in general fall into three categories: sensitive data, data to
be shared with other apps, and non-sensitive data not to be shared with others.
For the first and second type of data, Android has provided very good storage
models: an app's private sensitive data are saved to its private folder that
can only be access by the app itself, and the data to be shared are saved to
public storage (either the external SD card or the emulated SD card area on
internal FLASH memory). But for the last type, i.e., an app's non-sensitive and
non-shared data, there is a big problem in Android's current storage model
which essentially encourages an app to save its non-sensitive data to shared
public storage that can be accessed by other apps. At first glance, it seems no
problem to do so, as those data are non-sensitive after all, but it implicitly
assumes that app developers could correctly identify all sensitive data and
prevent all possible information leakage from private-but-non-sensitive data.
In this paper, we will demonstrate that this is an invalid assumption with a
thorough survey on information leaks of those apps that had followed Android's
recommended storage model for non-sensitive data. Our studies showed that
highly sensitive information from billions of users can be easily hacked by
exploiting the mentioned problematic storage model. Although our empirical
studies are based on a limited set of apps, the identified problems are never
isolated or accidental bugs of those apps being investigated. On the contrary,
the problem is rooted from the vulnerable storage model recommended by Android.
To mitigate the threat, we also propose a defense framework
Sofie: Smart Operating System For Internet Of Everything
The proliferation of Internet of Things and the success of rich cloud services have pushed the
horizon of a new computing paradigm, Edge computing, which calls for processing the data at
the edge of the network. Applications such as cloud offloading, smart home, and smart city
are idea area for Edge computing to achieve better performance than cloud computing. Edge
computing has the potential to address the concerns of response time requirement, battery life
constraint, bandwidth cost saving, as well as data safety and privacy.
However, there are still some challenges for applying Edge computing in our daily life. The
missing of the specialized operating system for Edge computing is holding back the flourish of
Edge computing applications. Service management, device management, component selection
as well as data privacy and security is also not well supported yet in the current computing
structure.
To address the challenges for Edge computing systems and applications in these aspects, we
have planned a series of empirical and theoretical research. We propose SOFIE: Smart Operating
System For Internet Of Everything. SOFIE is the operating system specialized for Edge
computing running on the Edge gateway. SOFIE could establish and maintain a reliable connection
between cloud and Edge device to handle the data transportation between gateway and
Edge devices; to provide service management and data management for Edge applications; to
protect data privacy and security for Edge users; to guarantee the wellness of the Edge devices.
Moreover, SOFIE also provide a naming mechanism to connect Edge device more efficiently.
To solve the component selection problem in Edge computing paradigm, SOFIE also include
our previous work, SURF, as a model to optimize the performance of the system. Finally,
we deployed the design of SOFIE on an IoT/M2M system and support semantics with access
control
An Exploratory Study of Patient Falls
Debate continues between the contribution of education level and clinical expertise in the nursing practice environment. Research suggests a link between Baccalaureate of Science in Nursing (BSN) nurses and positive patient outcomes such as lower mortality, decreased falls, and fewer medication errors. Purpose: To examine if there a negative correlation between patient falls and the level of nurse education at an urban hospital located in Midwest Illinois during the years 2010-2014? Methods: A retrospective crosssectional cohort analysis was conducted using data from the National Database of Nursing Quality Indicators (NDNQI) from the years 2010-2014. Sample: Inpatients aged ā„ 18 years who experienced a unintentional sudden descent, with or without injury that resulted in the patient striking the floor or object and occurred on inpatient nursing units. Results: The regression model was constructed with annual patient falls as the dependent variable and formal education and a log transformed variable for percentage of certified nurses as the independent variables. The model overall is a good fit, F (2,22) = 9.014, p = .001, adj. R2 = .40. Conclusion: Annual patient falls will decrease by increasing the number of nurses with baccalaureate degrees and/or certifications from a professional nursing board-governing body
Multimodal Classification of Urban Micro-Events
In this paper we seek methods to effectively detect urban micro-events. Urban
micro-events are events which occur in cities, have limited geographical
coverage and typically affect only a small group of citizens. Because of their
scale these are difficult to identify in most data sources. However, by using
citizen sensing to gather data, detecting them becomes feasible. The data
gathered by citizen sensing is often multimodal and, as a consequence, the
information required to detect urban micro-events is distributed over multiple
modalities. This makes it essential to have a classifier capable of combining
them. In this paper we explore several methods of creating such a classifier,
including early, late, hybrid fusion and representation learning using
multimodal graphs. We evaluate performance on a real world dataset obtained
from a live citizen reporting system. We show that a multimodal approach yields
higher performance than unimodal alternatives. Furthermore, we demonstrate that
our hybrid combination of early and late fusion with multimodal embeddings
performs best in classification of urban micro-events
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Who Dominate TDI? A Big Data Evidence from DMO and UGC Short Videos
Abstract
In the formation of tourism destination image (TDI), understanding and being able to measure, analyze, compare, and contrast the images projected by user-generated content(UGC) and destination marketing organizations(DMOs) is crucial in tourism management and destination marketing. This study aims to propose a specific way to measure and compare UGC and DMO projected images within 249 short videos. Using machine learning algorithms, four indicators (the number of short videos, the number of likes, comments and shares) were identified to measure the influences of DMO and UGC short videos, and extracted 7 dimensions representing the destination image extracted through video content analysis, namely, nature environment, infrastructure, culture and art, people, food and beverage, specific activities, and transportation. The data analysis further revealed statistical differences in several dimensions of these images at different destinationās life cycle. Theoretical and practical implications were also discussed.
Keywords
tourism destination image, destinationās life cycle, short video, video captainingļ¼brand hijac
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