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Lyceum: internet voice groupware for distance learning
This paper describes the design, implementation and deployment of Lyceum, a groupware system providing students and tutors with real time voice conferencing and visual workspace tools, over the standard internet. Lyceum uses a Java client/server architecture to tackle a formidable set of networking requirements: multi-way voice communication with synchronous shared displays, scalable to hundreds of simultaneous users, running over normal modem connections via unknown internet service providers, on home PCs. Additionally, the design had to support multiple courses with different requirements. We describe the interdisciplinary requirements analysis, and iterative design process, by which an academic course team was able to specify and evaluate prototypes. We present the systemís architecture, describe the technical successes and failures from Lyceumís first large scale deployment, and summarise its affordances for interaction and learning
parMERASA Multi-Core Execution of Parallelised Hard Real-Time Applications Supporting Analysability
International audienceEngineers who design hard real-time embedded systems express a need for several times the performance available today while keeping safety as major criterion. A breakthrough in performance is expected by parallelizing hard real-time applications and running them on an embedded multi-core processor, which enables combining the requirements for high-performance with timing-predictable execution. parMERASA will provide a timing analyzable system of parallel hard real-time applications running on a scalable multicore processor. parMERASA goes one step beyond mixed criticality demands: It targets future complex control algorithms by parallelizing hard real-time programs to run on predictable multi-/many-core processors. We aim to achieve a breakthrough in techniques for parallelization of industrial hard real-time programs, provide hard real-time support in system software, WCET analysis and verification tools for multi-cores, and techniques for predictable multi-core designs with up to 64 cores
Fast and scalable Gaussian process modeling with applications to astronomical time series
The growing field of large-scale time domain astronomy requires methods for
probabilistic data analysis that are computationally tractable, even with large
datasets. Gaussian Processes are a popular class of models used for this
purpose but, since the computational cost scales, in general, as the cube of
the number of data points, their application has been limited to small
datasets. In this paper, we present a novel method for Gaussian Process
modeling in one-dimension where the computational requirements scale linearly
with the size of the dataset. We demonstrate the method by applying it to
simulated and real astronomical time series datasets. These demonstrations are
examples of probabilistic inference of stellar rotation periods, asteroseismic
oscillation spectra, and transiting planet parameters. The method exploits
structure in the problem when the covariance function is expressed as a mixture
of complex exponentials, without requiring evenly spaced observations or
uniform noise. This form of covariance arises naturally when the process is a
mixture of stochastically-driven damped harmonic oscillators -- providing a
physical motivation for and interpretation of this choice -- but we also
demonstrate that it can be a useful effective model in some other cases. We
present a mathematical description of the method and compare it to existing
scalable Gaussian Process methods. The method is fast and interpretable, with a
range of potential applications within astronomical data analysis and beyond.
We provide well-tested and documented open-source implementations of this
method in C++, Python, and Julia.Comment: Updated in response to referee. Submitted to the AAS Journals.
Comments (still) welcome. Code available: https://github.com/dfm/celerit
parMERASA – multicore execution of parallelised hard real-time applications supporting analysability
Abstract-Engineers who design hard real-time embedded systems express a need for several times the performance available today while keeping safety as major criterion. A breakthrough in performance is expected by parallelizing hard real-time applications and running them on an embedded multi-core processor, which enables combining the requirements for high-performance with timing-predictable execution. parMERASA will provide a timing analyzable system of parallel hard real-time applications running on a scalable multicore processor. parMERASA goes one step beyond mixed criticality demands: It targets future complex control algorithms by parallelizing hard real-time programs to run on predictable multi-/many-core processors. We aim to achieve a breakthrough in techniques for parallelization of industrial hard real-time programs, provide hard real-time support in system software, WCET analysis and verification tools for multi-cores, and techniques for predictable multi-core designs with up to 64 cores
Deep Learning for Quality Assessment in Live Video Streaming
Video content providers put stringent requirements on the quality assessment methods realized on their services. They need to be accurate, real-time, adaptable to new content, and scal-able as the video set grows. In this letter, we introduce a novel automated and computationally efficient video assessment method. It enables accurate real-time (online) analysis of delivered quality in an adaptable and scalable manner. Offline deep unsupervised learning processes are employed at the server side and inexpensive no-reference measurements at the client side. This provides both real-time assessment and performance comparable to the full reference counterpart, while maintaining its no-reference characteristics. We tested our approach on the LIMP Video Quality Database (an extensive packet loss impaired video set) obtaining a correlation between 78% and 91% to the FR benchmark (the video quality metric). Due to its unsupervised learning essence, our method is flexible and dynamically adaptable to new content and scalable with the number of videos. Index Terms-Deep learning (DL), multimedia video services, unsupervised learning (UL), video quality assessment
Storage Solutions for Big Data Systems: A Qualitative Study and Comparison
Big data systems development is full of challenges in view of the variety of
application areas and domains that this technology promises to serve.
Typically, fundamental design decisions involved in big data systems design
include choosing appropriate storage and computing infrastructures. In this age
of heterogeneous systems that integrate different technologies for optimized
solution to a specific real world problem, big data system are not an exception
to any such rule. As far as the storage aspect of any big data system is
concerned, the primary facet in this regard is a storage infrastructure and
NoSQL seems to be the right technology that fulfills its requirements. However,
every big data application has variable data characteristics and thus, the
corresponding data fits into a different data model. This paper presents
feature and use case analysis and comparison of the four main data models
namely document oriented, key value, graph and wide column. Moreover, a feature
analysis of 80 NoSQL solutions has been provided, elaborating on the criteria
and points that a developer must consider while making a possible choice.
Typically, big data storage needs to communicate with the execution engine and
other processing and visualization technologies to create a comprehensive
solution. This brings forth second facet of big data storage, big data file
formats, into picture. The second half of the research paper compares the
advantages, shortcomings and possible use cases of available big data file
formats for Hadoop, which is the foundation for most big data computing
technologies. Decentralized storage and blockchain are seen as the next
generation of big data storage and its challenges and future prospects have
also been discussed
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