10.05 System for automatisk individuell faglig begrunnelse og tilbakemelding v/Omid Mirmotahari, førsteamanuensis, Studielaben, Institutt for informatikk, UiO
11.00 Adaptivt læringsverktøy for matematikk v/Siebe van Albada, studieprogramleder simulering og visualisering, IIR, NTNU
11.20 Modelleringsverktøy for dingser v/Adrian Rutle, studiekoordinator informasjonsteknologi, Institutt for data- og realfag, HVL
11.40 Aktiv læring i matematikk v/Hans Georg Schaathun, professor, SoftICE Lab, IIR, NTNU
Hovedinnlegget holdes av Omid Mirmotahari, som har vunnet en rekke priser for undervisning, forskning og formidling. Nylig har han også figurert i media med innovative “automagiske” system for automatisk individuell faglig begrunnelse og tilbakemelding:
Ottar L. Osen and Robin T. Bye. Reflections on teaching electrical and computer engineering courses at the bachelor level. In Proceedings of the 9th International Conference on Computer Supported Education (CSEDU ’17). INSTICC, SCITEPRESS, April 2017. Accepted for publication.
Robin T. Bye. The teacher as a facilitator for learning: Flipped classroom in a master’s course on artificial intelligence. In Proceedings of the 9th International Conference on Computer Supported Education (CSEDU ’17). INSTICC, SCITEPRESS, April 2017. Accepted for publication.
Reflections on teaching electrical and computer engineering courses at the bachelor level
This paper reflects on a number of observations the authors have made over many years of teaching courses in electrical and computer engineering bachelor programmes.
We suggest various methods and tips for improving lectures, attendance, group work, and compulsory coursework, and discuss aspects of facilitating active learning, focussing on simple in-classroom activities and larger problem-based activities such as assignments, projects, and laboratory work. Moreover, we identify solving real-world problems by means of practical application of relevant theory as key to achieving intended learning outcomes. Our observations and reflections are then put into a theoretical context, including students’ approaches of learning, constructive alignment, active learning, and problem-based versus problem-solving learning. Finally, we present and discuss some recent results from a student evaluation survey and draw some conclusions.
The teacher as a facilitator for learning: Flipped classroom in a master’s course on artificial intelligence
In this paper, I present a flipped classroom approach for teaching a master’s course on artificial intelligence. Traditional lectures in the classroom are outsourced to an open online course to free up valuable time for active, in-class learning activities. In addition, students design and implement intelligent algorithms for solving a variety of relevant problems cherrypicked from online game-like code development platforms. Learning activities are carefully chosen to align with intended learning outcomes, course curriculum, and assessment to allow for learning to be constructed by the students themselves under guidance by the teacher, much in accord with the theory of constructive alignment. Thus, the teacher acts as a facilitator for learning, much similar to that of a personal trainer or a coach. I present an overview of relevant literature, the course content and teaching methods, and a recent course evaluation, before I discuss some limiting frame factors and challenges with the approach and point to future work.
The Telenor-NTNU AI-Lab was officially opened on 8 March 2017, when several prominent guests, including Norwegian Minister of Trade and Industry Monica Mæland, Norwegian Minister of Culture Linda Hofstad Helleland, SINTEF CEO Alexandra Bech Gjørv, and Telenor CEO Sigve Brekke, amongst others, joined NTNU rector Gunnar Bovim and head of the Department of Computer Science at NTNU, Letizia Jaccheri for celebration.
Celebrity guests meets Inge, one of the SoftICE Lab’s social robots.
About the AI-Lab
The Telenor-NTNU AI-Lab is a joint lab for research in Artificial Intelligence, Machine Learning, and Big Data Analytics. The lab was established in 2016, and has been formally operative from January 1st, 2017. It is hosted by the Department of Computer Science. The lab will conduct fundamental ML research, including theory and method development, as well as application-oriented research at a high international level. Lab facilities will also be available for other research groups within the Faculty of Information Technology and Electrical Engineering (IE) doing ML research, for NTNU more generally, and for external cooperating partners.
Telenor-NTNU AI-Lab was established as part of Telenor’s vision to help Norway deal with the challenges of an increasing digitized society. The SoftICE Lab intends to be contribute to reaching this goal.
The AI-Lab will host its very first hackathon on the weekend 17-18 March both in Trondheim and at the SoftICE Lab on Campus Ålesund.
IEEE is the world’s largest technical professional organization with more than 420,000 members worldwide in over 160 countries and is dedicated to advancing technology for the benefit of humanity. IEEE produces over 30% of the world’s literature in the electrical and electronics engineering and computer science fields, publishing well over 100 peer-reviewed journals and sponsoring more than 1,600 annual conferences and meetings worldwide. In addition, IEEE is one of the leading standards-making organizations in the world through its IEEE Standards Association, with more than 900 active standards and over 500 standards under development as of 2013, including the IEEE 802.3Ethernet standard and the IEEE 802.11 Wireless Networking standard.
Upon meeting certain requirements, a professional member can apply for Senior Membership, which is the highest level of recognition that a professional member can directly apply for.
Applicants for Senior Member must have at least three letters of recommendation from Senior, Fellow, or Honorary members and fulfill other rigorous requirements of education, achievement, remarkable contribution, and experience in the field. The Senior Members are a selected group, and certain IEEE officer positions are available only to Senior (and Fellow) Members. Senior Membership is also one of the requirements for those who are nominated and elevated to the grade IEEE Fellow, a distinctive honour.
During the anniversial 30th European Conference on Modelling and Simulation (ECMS) 2016 in Regensburg, Germany on 31 May — 3 June, SoftICE members Robin T. Bye, Ottar L. Osen, and Ibrahim A. Hameed presented some of our latest research reported in four scientific papers accepted in the Simulators for Virtual Prototyping and Training track, which was chaired by colleagues at NTNU Ottar L. Osen, Robin T. Bye, and Henrique Gaspar (who unfortunately could not attend this time). The papers were co-authored by the three abovementioned researchers together with colleagues Hans Georg Schaathun and Birger Skogeng Pedersen (NTNU in Ålesund), Adrian Rutle (University College of Bergen), Filippo Sanfilippo (NTNU in Trondheim), and bachelor graduates Rolf-Magnus Hjørungdal (NTNU in Ålesund) and Tom Verplaetse (Ghent University).
Best Student Paper Award
All four papers were reviewed by three reviewers and received excellent reviews. One paper,“Intelligent computer-automated crane design using an online crane prototyping tool” by Ibrahim A. Hameed, Ottar L. Osen, Robin T. Bye, Birger Skogeng Pedersen, and Hans Georg Schaathun, was nominated for the Best Paper Award but did not make it to the top.
However, another paper, “On Usage of EEG Brain Control for Rehabilitation of Stroke Patients” by Tom Verplaetse, Filippo Sanfilippo, Adrian Rutle, Ottar L. Osen, and Robin T. Bye, was nominated for both the Best Paper Award and the Best Student Paper Award, and managed to win the latter! A big congratulation to first author Tom Verplaetse (currently a master in engineering student at Ghent University), who completed the project as his bachelor thesis in automation engineering at NTNU in Ålesund spring 2015! The award was received by Robin T. Bye as Tom was busy with his exams during the conference.
Proud delegation from NTNU in Ålesund with Best Student Paper Award.
Best Student Paper Award certificate.
Being a 30th anniversary jubilee conference, the board of the European Council of Modelling and Simulation decided to honour a number of people,including SoftICE member Robin T. Bye, with a Special Award for their contributions to ECMS over the years. Associate Professor Bye humbly accepted the award, which was presented at the conference dinner cruise on the river Donau aboard the luxurious ship Kristallkönigin. Dr. Bye received the award for his contributions as a previous board member of ECMS (2012-14), conference co-chair and programme chair of ECMS ’13 hosted by Aalesund University College (2013), and track chair activities (2012-16).
30th anniversary ECMS Special Award winners.
Special Award prize.
Proud SoftICE delegation with Special Award.
More details about the work, as well as links for downloading papers, abstracts, and presentations can be found in earlier blog posts:
SoftICE members Robin T. Bye, Ottar L. Osen, and Ibrahim A. Hameed will be presenting flaming hot research in four scientific papers to be presented at the 30th European Conference on Modelling and Simulation (ECMS) 2016 to be hold in Regensburg, Germany on 31 May — 3 June. The papers are co-authored by the three abovementioned researchers together with colleagues Hans Georg Schaathun and Birger Skogeng Pedersen (NTNU in Ålesund), Adrian Rutle (University College of Bergen), Filippo Sanfilippo (NTNU in Trondheim), and bachelor graduates Rolf-Magnus Hjørungdal (NTNU in Ålesund) and Tom Verplaetse (Ghent University).
Best paper award?
All four papers received excellent reviews by three independent reviewers, with one paper being nominated for the Best Paper Award and another paper being nominated for both Best Paper Award and Best Student Paper Award. Fingers crossed!
Intelligent computer-automated product design
Two of the papers relate to intelligent computer-automated product design, exemplified by a case study where we use methods from artificial intelligence (AI) such as genetic algorithms (GAs), particle swarm optimisation (PSO), and simulated annealing (SA) to optimise offshore crane design. Within a matter of only minutes, the algorithms are able to outperform the design of a real and delivered offshore crane with respect to some desired key performance indicators (KPIs). A human being would likelly spend days or weeks to obtain the same results.
Main components and load chart of a typical offshore crane.
EEG brain control (“mind control”)
The other two papers relate to EEG brain control, commonly known as “mind control,” for rehabilitation of stroke patients and for control of motorised, electrical wheelchairs.
These papers build on the work done during the bachelor thesis projects by Tom Verplaetse (Interfacing an EEG headset with a 3D simulation for rehabilitation in partially paraplegic stroke victims) and by Rolf-Magnus Hjørungdal and fellow students Fredrik Hoel Helgesen and Daniel Nedregård (Man/machine interaction through EEG).
Emotiv EPOC EEG headset for brain control.
We have previously presented some details of our work in earlier blog posts:
The titles of the four papers are listed further below, with abstracts, papers, and presentations readily available for download as indicated (also available here: http://robinbye.com | Publications).
Parties interested in research collaboration, testing our software, or more information are encouraged to contact us.
— SoftICE lab
List of ECMS 2016 papers and presentations
Robin T. Bye, Ottar L. Osen, Birger Skogeng Pedersen, Ibrahim A. Hameed, and Hans Georg Schaathun. A software framework for intelligent computer-automated product design. In Proceedings of the 30th European Conference on Modelling and Simulation (ECMS’16), pp. xx–yy, 2016. Download abstract | paper | presentation.
Ibrahim A. Hameed, Ottar L. Osen, Robin T. Bye, Birger Skogeng Pedersen, and Hans Georg Schaathun. Intelligent computer-automated crane design using an online crane prototyping tool. In Proceedings of the 30th European Conference on Modelling and Simulation (ECMS’16), pp. xx–yy, 2016. Download abstract | paper | presentation.
Tom Verplaetse, Filippo Sanfilippo, Adrian Rutle, Ottar L. Osen, and Robin T. Bye. On Usage of EEG Brain Control for Rehabilitation of Stroke Patients. In Proceedings of the 30th European Conference on Modelling and Simulation (ECMS’16), pp. xx–yy, 2016. Download abstract | paper | presentation.
Rolf-Magnus Hjørungdal, Filippo Sanfilippo, Ottar L. Osen, Adrian Rutle, and Robin T. Bye. A Game-based Learning Framework for Controlling Brain-Actuated Wheelchairs. In Proceedings of the 30th European Conference on Modelling and Simulation (ECMS’16), pp. xx–yy, 2016. Download abstract | paper | presentation.
SoftICE member Robin T. Bye will today present recent research on intelligent computer-automated product design. The talk is called A software framework for intelligent computer-automated product design and is a based on a recent paper that will be presented at the 30th European Conference on Modelling and Simulation (ECMS 2016) in Regensburg, Germany, in June. The paper has been co-authored with SoftICE members Ottar L. Osen, Birger Skogeng Pedersen, Ibrahim A. Hameed, and Hans Georg Schaathun.
The seminar is open for all and will take place in room Åse at 13.00 today 29 April 2016, NTNU in Ålesund main building.
This work is part of the research project Artificial Intelligence for Crane Design (Kunstig intelligens for krandesign (KIK)) funded by RFF/Research Council of Norway.
Architecture of software framework for intelligent computer-automated product design.
In the SoftICE lab, we have had several bachelor projects over the years that have examined how to use inexpensive commercial off-the-shelf (COTS) electroencephalography (EEG) equipment to enable brain control in virtual environments. Specifically, we have been using the scientific version of the Emotiv Epoc EEG headset, which has 14 sensors that measure raw EEG signals on top of the human scalp. These signals can be filtered (converted) in real-time to suitable control signals via the Emotiv software and passed on to virtual environments in the 3D game engine Unity, thus enabling real-time control of objects and characters in a virtual world only by the use of brain waves.
Screenshot of the Unity demo game Lerpz Escapes
In the first bachelor project we ran as early as 2011, our students were able to demonstrate a proof of concept by developing an interface between readings from the EEG headset and a demo game in Unity called Lerpz Escapes. After some training sessions for finetuning of personal Emotiv control profiles (the Emotiv control software needs to ‘learn’ the EEG signals of each individual user), the students were able to control a 3D third-person character in the computer game only by using their mind.
This year, we have had two bachelor projects going one step further from this initial work.
The first project was made by a group consisting of students Fredrik Hoel Helgesen, Rolf-Magnus Hjørungdal, and Daniel Nedregård, and was supervised by AAUC staff Robin T. Bye and Anders Sætersmoen, with additional insights provided by staff members Filippo Sanfilippo and Hans Georg Schaathun. The students used Unity to develop a virtual reality environment that can serve as a training platform for controlling a motorised wheelchair only by means of brain waves (EEG). Their work was inspired by patients who suffer from amyotrophic lateral sclerosis (ALS), which is also known as Lou Gehrig’s disease, and therefore gradually become completely paralysed and unable to control conventional electric wheelchairs using their hands or chin. Following a set of training sessions, users develop their brain control skills and are able to control a motorised wheelchair in realistic virtual environments with streets, buildings, pedestrians, trees, and so on.
The group also did some preliminary work using artificial neural networks to map the neural EEG signals to appropriate motor commands as well as examine using the Oculus Rift for virtual reality.
The source code is freely available on GitHub. The usual standards for citing, using and modifying scientific intellectual property apply.
The second project was made by international exchange student Tom Verplaetse (originally at University College Ghent, Belgium) and supervised by AAUC staff Robin T. Bye and Filippo Sanfilippo. Tom examined how one can use EEG control as a new rehabilitation technique for stroke victims who have lost the ability to move a single hand or both of their hands, a condition called partial paraplegia. Partial paraplegia can be healed by months or sometimes years of physical therapy and other therapies, and developing new rehabilitation techniques is an active field of research worldwide. In the work of Tom, the idea was to create a 3D environment in which the rehabilitating patient can move a visual representation of the paraplegic hand, thus achieving the same effect as that of mirror therapy. Mirror therapy relies on the ability to trick the brain into thinking it can move a hand that is not really there but is merely a visual representation.
The software developed in this project provides a 3D representation of that hand and lets the brain control it by using its own brain waves. Clever use of visual stimulation at specific frequencies by means of a flickering light led to steady state visually evoked potentials (SSVEP) that clearly enhanced both alpha and beta EEG activity.
Hopefully, this process of brain pattern recognition and brain activation of the specific regions needed for motor function could lead to a faster and more efficient rehabilitation process, without much need of expensive equipment or human helpers such as physioterapeuts or nurses.
Building on his PhD work in neuroengineering and modelling and simulation of human movement control systems, SoftICE member Robin T. Bye has together with researchers Peter D. Neilson and Megan D. Neilson from the Neuroengineering Laboratory at the University of New South Wales, Sydney, co-authored a paper about a Riemannian theory of the entire human body moving in a 3D environment. The paper details a highly mathematical intelligent control engineering approach using Riemannian geometry to develop the intuitive groundwork for a Riemannian field theory of human movement encompassing the entire body moving in gravity and in mechanical interaction with the environment. In particular we present a geodesic synergy hypothesis concerning planning of multi-joint coordinated movements to achieve goals with minimal muscular effort.
The full paper is published in the journal Human Movement Science and can be freely accssed from ScienceDirect.
Peter D. Neilson, Megan D. Neilson, and Robin T. Bye. A Riemannian Geometry Model of Human Movement: The Geodesic Synergy Hypothesis. Human Movement Science 44: 42–72, 2015.
Drawing of the two-DOF arm moving in the horizontal (x–y)-plane.
The MATLAB/Simulink geodesic trajectory generator (GTG) simulator for the two-DOF arm moving in the horizontal plane.
We develop a Riemannian theory of the entire human body moving in a 3D environment.
The theory addresses nonlinear inertial interactions within the body and externally.
Geometric concepts are explained intuitively to aid access for non-mathematicians.
We show how to plan geodesic synergies to achieve task goals with minimum effort.
We integrate the theory with previous descriptions of response planning and control.
Mass-inertia loads on muscles change with posture and with changing mechanical interactions between the body and the environment. The nervous system must anticipate changing mass-inertia loads, especially during fast multi-joint coordinated movements. Riemannian geometry provides a mathematical framework for movement planning that takes these inertial interactions into account. To demonstrate this we introduce the controlled (vs. biomechanical) degrees of freedom of the body as the coordinate system for a configuration space with movements represented as trajectories. This space is not Euclidean. It is endowed at each point with a metric equal to the mass-inertia matrix of the body in that configuration. This warps the space to become Riemannian with curvature at each point determined by the differentials of the mass-inertia at that point. This curvature takes nonlinear mass-inertia interactions into account with lengths, velocities, accelerations and directions of movement trajectories all differing from those in Euclidean space. For newcomers to Riemannian geometry we develop the intuitive groundwork for a Riemannian field theory of human movement encompassing the entire body moving in gravity and in mechanical interaction with the environment. In particular we present a geodesic synergy hypothesis concerning planning of multi-joint coordinated movements to achieve goals with minimal muscular effort.
What is the big deal of randomness? Randomness is the key to several common applications of computers, including games and secure communication to name but two of the most obvious ones. Games, such as lotteries and poker, are obviously supposed to be random. Imperfect randomness amounts to loading the dice. Secure communications may be less obvious to the uninitated, but the cryptography which is used to keep secrets depends on random keys to be secure. Loaded dice gives the attacker or intruder an edge. And obviously we want our banking and credit card transactions to be secure.
Programming randomness in computers was recognised as an importent challenge even in the early days of computing (around 1950), and it has been studied ever since. One would think the topic be exhausted by now. In fact, I believed enough had been written on computer randomness when I started this work in 2013.
In fact, randomness is well understood for application in sequential computer programs, i.e. a program which only utilises one of the CPU cores in the computer. A typical consumer-end computer these days has four. If you want to use all of them in one application, the software must be written for parallel execution.
True randomness is hard to achieve in a computer. It is possible to a certain degree, but a large number of random values take time to generate. Instead, the common solution is a so-called pseudo-random number generator (PRNG). Many exist. Most of them are flawed, but there are enough PRNG constructions which are considered trustworthy. However, the well-known solutions are all sequential. The random numbers are generated one by one in sequence. Distributing randomness across parallel threads (or multiple cores in the CPU to take a concrete view) is non-trivial.
Parallel randomness was recognised as an important problem already in the first half of the 1980-s. Yet, the literature is sparse. Some constructions have appeared, but few inspires any confidence. In fact, my paper demonstrates serious flaws in almost all of the known constructions. The first solution which inspires any confidence is that of Claessen and Pałka in 2013.