51,814 research outputs found
A Review of High School Level Astronomy Student Research Projects over the last two decades
Since the early 1990s with the arrival of a variety of new technologies, the
capacity for authentic astronomical research at the high school level has
skyrocketed. This potential, however, has not realized the bright-eyed hopes
and dreams of the early pioneers who expected to revolutionise science
education through the use of telescopes and other astronomical instrumentation
in the classroom. In this paper, a general history and analysis of these
attempts is presented. We define what we classify as an Astronomy Research in
the Classroom (ARiC) project and note the major dimensions on which these
projects differ before describing the 22 major student research projects active
since the early 1990s. This is followed by a discussion of the major issues
identified that affected the success of these projects and provide suggestions
for similar attempts in the future.Comment: Accepted for Publication in PASA. 26 page
The Implications of Diverse Applications and Scalable Data Sets in Benchmarking Big Data Systems
Now we live in an era of big data, and big data applications are becoming
more and more pervasive. How to benchmark data center computer systems running
big data applications (in short big data systems) is a hot topic. In this
paper, we focus on measuring the performance impacts of diverse applications
and scalable volumes of data sets on big data systems. For four typical data
analysis applications---an important class of big data applications, we find
two major results through experiments: first, the data scale has a significant
impact on the performance of big data systems, so we must provide scalable
volumes of data sets in big data benchmarks. Second, for the four applications,
even all of them use the simple algorithms, the performance trends are
different with increasing data scales, and hence we must consider not only
variety of data sets but also variety of applications in benchmarking big data
systems.Comment: 16 pages, 3 figure
MOSDEN: An Internet of Things Middleware for Resource Constrained Mobile Devices
The Internet of Things (IoT) is part of Future Internet and will comprise
many billions of Internet Connected Objects (ICO) or `things' where things can
sense, communicate, compute and potentially actuate as well as have
intelligence, multi-modal interfaces, physical/ virtual identities and
attributes. Collecting data from these objects is an important task as it
allows software systems to understand the environment better. Many different
hardware devices may involve in the process of collecting and uploading sensor
data to the cloud where complex processing can occur. Further, we cannot expect
all these objects to be connected to the computers due to technical and
economical reasons. Therefore, we should be able to utilize resource
constrained devices to collect data from these ICOs. On the other hand, it is
critical to process the collected sensor data before sending them to the cloud
to make sure the sustainability of the infrastructure due to energy
constraints. This requires to move the sensor data processing tasks towards the
resource constrained computational devices (e.g. mobile phones). In this paper,
we propose Mobile Sensor Data Processing Engine (MOSDEN), an plug-in-based IoT
middleware for mobile devices, that allows to collect and process sensor data
without programming efforts. Our architecture also supports sensing as a
service model. We present the results of the evaluations that demonstrate its
suitability towards real world deployments. Our proposed middleware is built on
Android platform
Advanced Cloud Privacy Threat Modeling
Privacy-preservation for sensitive data has become a challenging issue in
cloud computing. Threat modeling as a part of requirements engineering in
secure software development provides a structured approach for identifying
attacks and proposing countermeasures against the exploitation of
vulnerabilities in a system . This paper describes an extension of Cloud
Privacy Threat Modeling (CPTM) methodology for privacy threat modeling in
relation to processing sensitive data in cloud computing environments. It
describes the modeling methodology that involved applying Method Engineering to
specify characteristics of a cloud privacy threat modeling methodology,
different steps in the proposed methodology and corresponding products. We
believe that the extended methodology facilitates the application of a
privacy-preserving cloud software development approach from requirements
engineering to design
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