Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

Advantages and challenges associated with augmented reality for education: A systematic review of the literature

In the era of the Internet of Things (IoT), an enormous amount of sensing devices collect and/or generate various sensory data over time for a wide range of fields and applications. Based on the nature of the application, these devices will result in big or fast/real-time data streams. Applying analytics over such data streams to discover new information, predict future insights, and make control decisions is a crucial process that makes IoT a worthy paradigm for businesses and a quality-of-life improving technology. In this paper, we provide a thorough overview on using a class of advanced machine learning techniques, namely deep learning (DL), to facilitate the analytics and learning in the IoT domain. We start by articulating IoT data characteristics and identifying two major treatments for IoT data from a machine learning perspective, namely IoT big data analytics and IoT streaming data analytics. We also discuss why DL is a promising approach to achieve the desired analytics in these types of data and applications. The potential of using emerging DL techniques for IoT data analytics are then discussed, and its promises and challenges are introduced. We present a comprehensive background on different DL architectures and algorithms. We also analyze and summarize major reported research attempts that leveraged DL in the IoT domain. The smart IoT devices that have incorporated DL in their intelligence background are also discussed. DL implementation approaches on the fog and cloud centers in support of IoT applications are also surveyed. Finally, we shed light on some challenges and potential directions for future research. At the end of each section, we highlight the lessons learned based on our experiments and review of the recent literature.

Deep Learning for IoT Big Data and Streaming Analytics: A Survey

Three Types of Interaction

In recent years, there has been an increasing interest in applying Augmented Reality (AR) to create unique educational settings. So far, however, there is a lack of review studies with focus on investigating factors such as: the uses, advantages, limitations, effectiveness, challenges and features of augmented reality in educational settings. Personalization for promoting an inclusive learning using AR is also a growing area of interest. This paper reports a systematic review of literature on augmented reality in educational settings considering the factors mentioned before. In total, 32 studies published between 2003 and 2013 in 6 indexed journals were analyzed. The main findings from this review provide the current state of the art on research in AR in education. Furthermore, the paper discusses trends and the vision towards the future and opportunities for further research in augmented reality for educational settings.

https://www.jstor.org/stable/jeductechsoci.17.4.133

Augmented Reality Trends in Education: A Systematic Review of Research and Applications.

In this paper, the authors show that augmented reality technology has a positive impact on the motivation of middle-school students. The Instructional Materials Motivation Survey (IMMS) (Keller, 2010) based on the ARCS motivation model (Keller, 1987a) was used to gather information; it considers four motivational factors: attention, relevance, confidence, and satisfaction. Motivational factors of attention and satisfaction in an augmented-reality-based learning environment were better rated than those obtained in a slides-based learning environment. When the impact of the augmented reality system was analyzed in isolation, the attention and confidence factors were the best rated. The usability study showed that although this technology is not mature enough to be used massively in education, enthusiasm of middle-school students diminished most of the barriers found.

/pdf/impact-of-an-augmented-reality-system-on-students-motivation-2gi4c7c60z.pdf

Impact of an augmented reality system on students' motivation for a visual art course

Educational researchers have recognized Augmented Reality (AR) as a technology with great potential to impact affective and cognitive learning outcomes. However, very little work has been carried out to substantiate these claims. The purpose of this study was to assess to which extent an AR learning application affects learners' level of enjoyment and learning effectiveness. The study followed an experimental/control group design using the type of the application (AR-based, web-based) as independent variable. 64 high school students were randomly assigned to the experimental or control group to learn the basic principles of electromagnetism. The participants' knowledge acquisition was evaluated by comparing pre- and post-tests. The participants' level overall-state perception on flow was measured with the Flow State Scale and their flow states were monitored throughout the learning activity. Finally, participants' perceptions of benefits and difficulties of using the augmented reality application in this study were qualitatively identified. The results showed that the augmented reality approach was more effective in promoting students' knowledge of electromagnetic concepts and phenomena. The analysis also indicated that the augmented reality application led participants to reach higher flow experience levels than those achieved by users of the web-based application. However, not all the factors seem to have influence on learners' flow state, this study found that they were limited to: concentration, distorted sense of time, sense of control, clearer direct feedback, and autotelic experience. A deeper analysis of the flow process showed that neither of the groups reported being in flow in those tasks that were very easy or too difficult. However, for those tasks that were not perceived as difficult and included visualization clues, the experimental group showed higher levels of flow that the control group. The study suggests that augmented reality can be exploited as an effective learning environment for learning the basic principles of electromagnetism at high school provided that learning designers strike a careful balance between AR support and task difficulty.

/pdf/experimenting-with-electromagnetism-using-augmented-reality-4i2qn6fb5d.pdf

Experimenting with electromagnetism using augmented reality: Impact on flow student experience and educational effectiveness

The aim of this study was to investigate the attitude of learners toward an augmented reality learning activity designed to help engineering students to solve an electromagnetic problem. The sample was 122 students. Students were asked to complete a survey questionnaire based on the Technology Acceptance Model (TAM) enhanced with perceived enjoyment items. The results of the evaluation show that intention of use the system is dependent of perceived enjoyment but not from perceived usefulness of the learning tool.

The Acceptance of Learning Augmented Reality Environments: A Case Study

Visualization of electromagnetism phenomena is difficult to achieve for learners with low spatial abilities. We are addressing this difficulty by using augmented reality interfaces to create 3D magnetic field representations as aid to student conceptualization about electromagnetic phenomena. Results of the study suggest that students with low spatial abilities perceive more enjoyment when using the AR application than those who had slightly better spatial abilities.

Angela Di Serio

Papers

Impact of an augmented reality system on students' motivation for a visual art course

Experimenting with electromagnetism using augmented reality: Impact on flow student experience and educational effectiveness

The Acceptance of Learning Augmented Reality Environments: A Case Study

Impact of Visuospatial Abilities on Perceived Enjoyment of Students toward an AR-Simulation System in a Physics Course

Instructional Materials Motivation Survey--Modified Version