This document provides instructions for using the Motion app to create videos. It explains that the user can begin by clicking on a template, which will open a window showing examples of different templates. The user can then preview and select a template, as well as choose a theme. Once a template is chosen, the user can double click to open it and begin editing by adding filters, changing text fonts and colors, and inserting shapes to create dramatic video effects for their storyline.
This product provides interesting possibilities for developing teaching and learning in schools toward a more collaborative direction with ICT. In this sense, according to Dooly (2008) Collaborative learning shifts the responsibility for learning to the student, in the role of “researcher” and self-directed learner. In order to work towards a collaborative learning approach, the teacher must fully understand their students’ preferred learning styles and their own conceptions of learning. This can help the teacher decide where and how to start an online cooperative/collaborative project. (Maricarmen Gamero and Yohimar Sivira, 2014)
This product provides interesting possibilities for developing teaching and learning in schools toward a more collaborative direction with ICT. In this sense, according to Dooly (2008) Collaborative learning shifts the responsibility for learning to the student, in the role of “researcher” and self-directed learner. In order to work towards a collaborative learning approach, the teacher must fully understand their students’ preferred learning styles and their own conceptions of learning. This can help the teacher decide where and how to start an online cooperative/collaborative project. (Maricarmen Gamero and Yohimar Sivira, 2014)
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
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From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
1. How to use Motion
Click on the
template to
begin with
2. Then you will be shown
this window, there are
examples of different
templates.
On the top right hand
corner a preview of the
template would be
shown when selected.
In this column, there
are several themes to
choose from
When your happy with the chosen
template, double click to open the
window
3. Select Add
filter and I
chose glow
with Aura,
which created
this effect of a
distorted feel
which adds on
dramatic
effect with our
storyline.
4. You can edit the text
with different fonts,
type, color and add
effects to the titles;
either by
Adding filters, shapes,
etc.
