12,513 research outputs found
City Data Fusion: Sensor Data Fusion in the Internet of Things
Internet of Things (IoT) has gained substantial attention recently and play a
significant role in smart city application deployments. A number of such smart
city applications depend on sensor fusion capabilities in the cloud from
diverse data sources. We introduce the concept of IoT and present in detail ten
different parameters that govern our sensor data fusion evaluation framework.
We then evaluate the current state-of-the art in sensor data fusion against our
sensor data fusion framework. Our main goal is to examine and survey different
sensor data fusion research efforts based on our evaluation framework. The
major open research issues related to sensor data fusion are also presented.Comment: Accepted to be published in International Journal of Distributed
Systems and Technologies (IJDST), 201
Data-Driven and Deep Learning Methodology for Deceptive Advertising and Phone Scams Detection
The advance of smartphones and cellular networks boosts the need of mobile
advertising and targeted marketing. However, it also triggers the unseen
security threats. We found that the phone scams with fake calling numbers of
very short lifetime are increasingly popular and have been used to trick the
users. The harm is worldwide. On the other hand, deceptive advertising
(deceptive ads), the fake ads that tricks users to install unnecessary apps via
either alluring or daunting texts and pictures, is an emerging threat that
seriously harms the reputation of the advertiser. To counter against these two
new threats, the conventional blacklist (or whitelist) approach and the machine
learning approach with predefined features have been proven useless.
Nevertheless, due to the success of deep learning in developing the highly
intelligent program, our system can efficiently and effectively detect phone
scams and deceptive ads by taking advantage of our unified framework on deep
neural network (DNN) and convolutional neural network (CNN). The proposed
system has been deployed for operational use and the experimental results
proved the effectiveness of our proposed system. Furthermore, we keep our
research results and release experiment material on
http://DeceptiveAds.TWMAN.ORG and http://PhoneScams.TWMAN.ORG if there is any
update.Comment: 6 pages, TAAI 2017 versio
A Framework for the Analysis and User-Driven Evaluation of Trust on the Semantic Web
This project will examine the area of trust on the Semantic Web and develop a framework for publishing and verifying trusted Linked Data. Linked Data describes a method of publishing structured data, automatically readable by computers, which can linked to other heterogeneous data with the purpose of becoming more useful. Trust plays a significant role in the adoption of new technologies and even more so in a sphere with such vast amounts of publicly-created data. Trust is paramount to the effective sharing and communication of tacit knowledge (Hislop, 2013). Up to now, the area of trust in Linked Data has not been adequately addressed, despite the Semantic Web stack having included a trust layer from the very beginning (Artz and Gil, 2007). Some of the most accurate data on the Semantic Web lies practically unused, while some of the most used linked data has high numbers of errors (Zaveri et al., 2013). Many of the datasets and links that exist on the Semantic Web are out of date and/or invalid and this undermines the credibility and validity, and ultimately, the trustworthiness of both the dataset and the data provider (Rajabi et al., 2012). This research will examine a number of datasets to determine the quality metrics that a dataset is required to meet to be considered ‘trusted’. The key findings will be assessed and utilized in the creation of a learning tool and a framework for creating trusted Linked Data
User centric community clouds
With the evolution in cloud technologies, users are becoming acquainted with seamless service provision. Nevertheless, clouds are not a user centric technology, and users become completely dependent on service providers. We propose a novel concept for clouds, where users self-organize to create their clouds. We present such an architecture for user-centric clouds, which relies on self-managed clouds based on doctrine and on identity management concepts
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