Power to the Learner: Towards Human-Intuitive and Integrative Recommendations with Open Educational Resources

Educational recommenders have received much less attention in comparison with e-commerce- and entertainment-related recommenders, even though efficient intelligent tutors could have potential to improve learning gains and enable advances in education that are essential to achieving the world’s sustainability agenda. Through this work, we make foundational advances towards building a state-aware, integrative educational recommender. The proposed recommender accounts for the learners’ interests and knowledge at the same time as content novelty and popularity, with the end goal of improving predictions of learner engagement in a lifelong-learning educational video platform. Towards achieving this goal, we (i) formulate and evaluate multiple probabilistic graphical models to capture learner interest; (ii) identify and experiment with multiple probabilistic and ensemble approaches to combine interest, novelty, and knowledge representations together; and (iii) identify and experiment with different hybrid recommender approaches to fuse population-based engagement prediction to address the cold-start problem, i.e., the scarcity of data in the early stages of a user session, a common challenge in recommendation systems. Our experiments with an in-the-wild interaction dataset of more than 20,000 learners show clear performance advantages by integrating content popularity, learner interest, novelty, and knowledge aspects in an informational recommender system, while preserving scalability. Our recommendation system integrates a human-intuitive representation at its core, and we argue that this transparency will prove important in efforts to give agency to the learner in interacting, collaborating, and governing their own educational algorithms

Exploring and Evaluating the Scalability and Efficiency of Apache Spark using Educational Datasets

Research into the combination of data mining and machine learning technology with web-based education systems (known as education data mining, or EDM) is becoming imperative in order to enhance the quality of education by moving beyond traditional methods. With the worldwide growth of the Information Communication Technology (ICT), data are becoming available at a significantly large volume, with high velocity and extensive variety. In this thesis, four popular data mining methods are applied to Apache Spark, using large volumes of datasets from Online Cognitive Learning Systems to explore the scalability and efficiency of Spark. Various volumes of datasets are tested on Spark MLlib with different running configurations and parameter tunings. The thesis convincingly presents useful strategies for allocating computing resources and tuning to take full advantage of the in-memory system of Apache Spark to conduct the tasks of data mining and machine learning. Moreover, it offers insights that education experts and data scientists can use to manage and improve the quality of education, as well as to analyze and discover hidden knowledge in the era of big data

Micro-manufacturing : research, technology outcomes and development issues

Besides continuing effort in developing MEMS-based manufacturing techniques, latest effort in Micro-manufacturing is also in Non-MEMS-based manufacturing. Research and technological development (RTD) in this field is encouraged by the increased demand on micro-components as well as promised development in the scaling down of the traditional macro-manufacturing processes for micro-length-scale manufacturing. This paper highlights some EU funded research activities in micro/nano-manufacturing, and gives examples of the latest development in micro-manufacturing methods/techniques, process chains, hybrid-processes, manufacturing equipment and supporting technologies/device, etc., which is followed by a summary of the achievements of the EU MASMICRO project. Finally, concluding remarks are given, which raise several issues concerning further development in micro-manufacturing

A Hadoop distribution for engineering simulation

— In this paper, we discuss on the VELaSSCo project (Visualization for Extremely LArge-Scale Scientific Computing). This project aims to develop a specific platform to store scientific data for FEM (Finite Element Method) and DEM (Discrete Element Method) simulations. Both of these simulations are used by the engineering community to evaluate the behavior of a 3D object (for example fluid simulation in a silo). These simulations produce large files, which are composed of different time steps of a simulation. But the amount of produced data is too big to fit into a single node. Some strategies decompose data between nodes, but after several time-steps, some data has to be scratched to free memory. In this project, we aim to develop a platform, which enables the scientific community to store huge amounts of data on any kind of IT systems. We target to store data on any IT systems because most of scientists have access to modern computation nodes and not huge storage nodes. Our platform will try to fill the gap between both worlds. In this paper, we give an overview of the VELaSSCo project, and we detail our platform and deployment software. This platform can be deployed on any kind of IT system (dedicated storage nodes, HPC nodes, etc.). This platform is specially designed to store data from DEM and FEM simulations. In this paper, we present a performance analysis of our deployment tool compared to the well-defined myHadoop tool. With our tool we are able to increase computation capabilities with containers and virtualization

Horizons of modern molecular dynamics simulation in digitalized solid freeform fabrication with advanced materials

Our ability to shape and finish a component by combined methods of fabrication including (but not limited to) subtractive, additive, and/or no theoretical mass-loss/addition during the fabrication is now popularly known as solid freeform fabrication (SFF). Fabrication of a telescope mirror is a typical example where grinding and polishing processes are first applied to shape the mirror, and thereafter, an optical coating is usually applied to enhance its optical performance. The area of nanomanufacturing cannot grow without a deep knowledge of the fundamentals of materials and consequently, the use of computer simulations is now becoming ubiquitous. This article is intended to highlight the most recent advances in the computation benefit specific to the area of precision SFF as these systems are traversing through the journey of digitalization and Industry-4.0. Specifically, this article demonstrates that the application of the latest materials modelling approaches, based on techniques such as molecular dynamics, are enabling breakthroughs in applied precision manufacturing techniques

Review of modern business intelligence and analytics in 2015: How to tame the big data in practice?: Case study - What kind of modern business intelligence and analytics strategy to choose?

The objective of this study was to find out the state of art architecture of modern business intelligence and analytics. Furthermore the status quo of business intelligence and analytics' architecture in an anonymous case company was examined. Based on these findings a future strategy was designed to guide the case company towards a better business intelligence and analytics environment. This objective was selected due to an increasing interest on big data topic. Thus the understanding on how to move on from traditional business intelligence practices to modern ones and what are the available options were seen as the key questions to be solved in order to gain competitive advantage for any company in near future. The study was conducted as a qualitative single-case study. The case study included two parts: an analytics maturity assessment, and an analysis of business intelligence and analytics' architecture. The survey included over 30 questions and was sent to 25 analysts and other individuals who were using a significant time to deal with or read financial reports like for example managers. The architecture analysis was conducted by gathering relevant information on high level. Furthermore a big picture was drawn to illustrate the architecture. The two parts combined were used to construct the actual current maturity level of business intelligence and analytics in the case company. Three theoretical frameworks were used: first framework regarding the architecture, second framework regarding the maturity level and third framework regarding reporting tools. The first higher level framework consisted of the modern data warehouse architecture and Hadoop solution from D'Antoni and Lopez (2014). The second framework included the analytics maturity assessment from the data warehouse institute (2015). Finally the third framework analyzed the advanced analytics tools from Sallam et al. (2015). The findings of this study suggest that modern business intelligence and analytics solution can include both data warehouse and Hadoop components. These two components are not mutually exclusive. Instead Hadoop is actually augmenting data warehouse to another level. This thesis shows how companies can evaluate their current maturity level and design a future strategy by benchmarking their own actions against the state of art solution. To keep up with the fast pace of development, research must be continuous. Therefore in future for example a study regarding a detailed path of implementing Hadoop would be a great addition to this field

Exploiting multiple levels of parallelism of Convergent Cross Mapping

Identifying causal relationships between variables remains an essential problem across various scientific fields. Such identification is particularly important but challenging in complex systems, such as those involving human behaviour, sociotechnical contexts, and natural ecosystems. By exploiting state space reconstruction via lagged embeddings of time series, convergent cross mapping (CCM) serves as an important method for addressing this problem. While powerful, CCM is computationally costly; moreover, CCM results are highly sensitive to several parameter values. Current best practice involves performing a systematic search on a range of parameters, but results in high computational burden, which mainly raises barriers to practical use. In light of both such challenges and the growing size of commonly encountered datasets from complex systems, inferring the causality with confidence using CCM in a reasonable time becomes a biggest challenge. In this thesis, I investigate the performance associated with a variety of parallel techniques (CUDA, Thrust, OpenMP, MPI and Spark, etc.,) to accelerate convergent cross mapping. The performance of each method was collected and compared across multiple experiments to further evaluate potential bottlenecks. Moreover, the work deployed and tested combinations of these techniques to more thoroughly exploit available computation resources. The results obtained from these experiments indicate that GPUs can only accelerate the CCM algorithm under certain circumstances and requirements. Otherwise, the overhead of data transfer and communication can become the limiting bottleneck. On the other hand, in cluster computing, the MPI/OpenMP framework outperforms the Spark framework by more than one order of magnitude in terms of processing speed and provides more consistent performance for distributed computing. This also reflects the large size of the output from the CCM algorithm. However, Spark shows better cluster infrastructure management, ease of software engineering, and more ready handling of other aspects, such as node failure and data replication. Furthermore, combinations of GPU and cluster frameworks are deployed and compared in GPU/CPU clusters. An apparent speedup can be achieved in the Spark framework, while extra time cost is incurred in the MPI/OpenMP framework. The underlying reason reflects the fact that the code complexity imposed by GPU utilization cannot be readily offset in the MPI/OpenMP framework. Overall, the experimental results on parallelized solutions have demonstrated a capacity for over an order of magnitude performance improvement when compared with the widely used current library rEDM. Such economies in computation time can speed learning and robust identification of causal drivers in complex systems. I conclude that these parallel techniques can achieve significant improvements. However, the performance gain varies among different techniques or frameworks. Although the use of GPUs can accelerate the application, there still exists constraints required to be taken into consideration, especially with regards to the input data scale. Without proper usage, GPUs use can even slow down the whole execution time. Convergent cross mapping can achieve a maximum speedup by adopting the MPI/OpenMP framework, as it is suitable to computation-intensive algorithms. By contrast, the Spark framework with integrated GPU accelerators still offers low execution cost comparing to the pure Spark version, which mainly fits in data-intensive problems

Semantic Systems. The Power of AI and Knowledge Graphs

This open access book constitutes the refereed proceedings of the 15th International Conference on Semantic Systems, SEMANTiCS 2019, held in Karlsruhe, Germany, in September 2019. The 20 full papers and 8 short papers presented in this volume were carefully reviewed and selected from 88 submissions. They cover topics such as: web semantics and linked (open) data; machine learning and deep learning techniques; semantic information management and knowledge integration; terminology, thesaurus and ontology management; data mining and knowledge discovery; semantics in blockchain and distributed ledger technologies

The Medium Is the Monster: Canadian Adaptations of Frankenstein and the Discourse of Technology

Technology, a word that emerged historically first to denote the study of any art or technique, has come, in modernity, to describe advanced machines, industrial systems, and media. McCutcheon argues that it is Mary Shelley’s 1818 novel Frankenstein that effectively reinvented the meaning of the word for modern English. It was then Marshall McLuhan’s media theory and its adaptations in Canadian popular culture that popularized, even globalized, a Frankensteinian sense of technology. The Medium Is the Monster shows how we cannot talk about technology – that human-made monstrosity – today without conjuring Frankenstein, thanks in large part to its Canadian adaptations by pop culture icons such as David Cronenberg, William Gibson, Margaret Atwood, and Deadmau5. In the unexpected connections illustrated by The Medium Is the Monster, McCutcheon brings a fresh approach to studying adaptations, popular culture, and technology.Athabasca University, Awards to Scholarly Publishing Program, Social Science & Humanities Research Counci