title: Learning collocations with FLAX apps

authors: Yu, Alex

subjects: computer science

abstract: The rise of Mobile Assisted Language Learning has brought a new dimension and dynamic into language classes. Game-like apps have become a particularly effective way to promote self-learning to young learners outside classroom. This paper describes a system called FLAX that allows teachers to automatically generate a variety of collocation games from a con-temporary collocation database built from Wikipedia text. These games are fun to play and mimic traditional classroom activities such as Collocation Matching, Collocation Guessing, Collocation Dominoes, and Related Words. The apps can be downloaded onto Android devices from the Google Play store, and exercises are automatically updated whenever new materials are added by teachers through a web-based interface on the FLAX server. Teachers have used these games to provide supplementary material for several Massive Open Online courses (MOOC) in Law discipline.

text: Learning collocations with FLAX apps The rise of Mobile Assisted Language Learning has brought a new dimension and dynamic into language classes. Game-like apps have become a particularly effective way to promote self-learning to young learners outside classroom. This paper describes a system called FLAX that allows teachers to automatically generate a variety of collocation games from a con-temporary collocation database built from Wikipedia text. These games are fun to play and mimic traditional classroom activities such as Collocation Matching, Collocation Guessing, Collocation Dominoes, and Related Words. The apps can be downloaded onto Android devices from the Google Play store, and exercises are automatically updated whenever new materials are added by teachers through a web-based interface on the FLAX server. Teachers have used these games to provide supplementary material for several Massive Open Online courses (MOOC) in Law discipline.

year: 2016

title: Evaluating earthquake hazard and risk using liquefied volcanic-ash layers in lakes

authors: Lowe, David J.

subjects: tephra seismites

abstract: Liquefied volcanic-ash or tephra layers, for which we have coined a new term, ‘tephra seismites’, are preserved in lake sediments in numerous 20,000-year-old lakes in the Hamilton Basin in northern North Island. Up to five tephra layers in cores taken from the lakes show signs of liquefaction - the phenomenon seen during the Christchurch earthquakes - that include disrupted tephra layers, voids, and dykes (injectites). These features show that one or more of four newly-discovered ‘hidden’ faults in Hamilton (see map) were responsible for previously unrecognised prehistoric earthquakes within the past 20,000 years (Kleyberg et al., 2015). Around 30 lakes are scattered amidst the faults, and hence cores are being taken from almost all of these to build up a picture of which fault(s) may have been active in that time. We anticipate that lakes closest to faults that have been active will show more tephra seismites than those farther from the faults.

text: Evaluating earthquake hazard and risk using liquefied volcanic-ash layers in lakes Liquefied volcanic-ash or tephra layers, for which we have coined a new term, ‘tephra seismites’, are preserved in lake sediments in numerous 20,000-year-old lakes in the Hamilton Basin in northern North Island. Up to five tephra layers in cores taken from the lakes show signs of liquefaction - the phenomenon seen during the Christchurch earthquakes - that include disrupted tephra layers, voids, and dykes (injectites). These features show that one or more of four newly-discovered ‘hidden’ faults in Hamilton (see map) were responsible for previously unrecognised prehistoric earthquakes within the past 20,000 years (Kleyberg et al., 2015). Around 30 lakes are scattered amidst the faults, and hence cores are being taken from almost all of these to build up a picture of which fault(s) may have been active in that time. We anticipate that lakes closest to faults that have been active will show more tephra seismites than those farther from the faults.

year: 2021

title: Mana Motuhake Ringa: The non-invasive neural interface based artificial hand

authors: Owen, Mahonri W.

subjects: Prosthetics

abstract: Ten million people on the earth at any given time suffer from the loss or lack of a limb. Three million of these are upper extremity amputees. No matter the cause or reason for amputation the amputees’ life is never the same again. Independence can be lost and never regained. The loss of a limb can be an overpowering event that effects the mental, social, and physical well-being of a person. Although there has been many attempts to restore hand function through neural prosthetics there is little to no credible solutions to regain it. This thesis presents methods to restore hand function based on the human grasping system and addresses key challenges in the practical application of those methods. The contribution of this work lies in improving artificial hand function by mimicking the human grasping system. The human grasping system consists of the brain, the nervous system and the hand. The methods used investigate: biomechanical design principles for artificial hands, the limitations of machine learning in non-invasive neural interfaces (NI’s) and the development of an autonomous hand control framework for artificial hand control. 

The application of the Support Vector Machine (SVM) to a non-invasive neural interface is principally limited to binary applications in the real world. In order to overcome this limitation an autonomous approach is developed to achieve better response and accuracy from NI control frameworks. In binary applications the SVM performed well with a classification accuracy of around 80% accompanied by a response time of just under 5 seconds. When applied to multiple classifications the SVM decreased in accuracy and response dramatically. The accuracy of the autonomous hand control framework had a classification accuracy of 95% with a response time comparable to that of the SVM. This outcome has the potential to move non-invasive NI research into the real world and to restore hand function in an intuitive and meaningful way. A key learning of this research is that machine learning methods for non-invasive neural interfaces are not the best way to restore hand function for amputees. Hand function is best restored through the combining of machine learning and machine vision into a single NI based control framework. Additionally, this work is based in Mātauranga Māori with a western science approach. By so doing, amputees will be empowered, have increased independence and enjoy a better quality of life.

The scope of this work is limited to the consideration of hand function restoration for upper extremity amputees. In particular trans-radial amputees. Focus is given to non-invasive methods of control, therefore, investigations are limited to the electroencephalography (EEG) signal. Mimicking the human grasping system is a complex and difficult process, therefore, the attempts in this work to restore hand function only consider mechanisms and processes related directly to the motor control of the human hand.

text: Mana Motuhake Ringa: The non-invasive neural interface based artificial hand Ten million people on the earth at any given time suffer from the loss or lack of a limb. Three million of these are upper extremity amputees. No matter the cause or reason for amputation the amputees’ life is never the same again. Independence can be lost and never regained. The loss of a limb can be an overpowering event that effects the mental, social, and physical well-being of a person. Although there has been many attempts to restore hand function through neural prosthetics there is little to no credible solutions to regain it. This thesis presents methods to restore hand function based on the human grasping system and addresses key challenges in the practical application of those methods. The contribution of this work lies in improving artificial hand function by mimicking the human grasping system. The human grasping system consists of the brain, the nervous system and the hand. The methods used investigate: biomechanical design principles for artificial hands, the limitations of machine learning in non-invasive neural interfaces (NI’s) and the development of an autonomous hand control framework for artificial hand control. 

The application of the Support Vector Machine (SVM) to a non-invasive neural interface is principally limited to binary applications in the real world. In order to overcome this limitation an autonomous approach is developed to achieve better response and accuracy from NI control frameworks. In binary applications the SVM performed well with a classification accuracy of around 80% accompanied by a response time of just under 5 seconds. When applied to multiple classifications the SVM decreased in accuracy and response dramatically. The accuracy of the autonomous hand control framework had a classification accuracy of 95% with a response time comparable to that of the SVM. This outcome has the potential to move non-invasive NI research into the real world and to restore hand function in an intuitive and meaningful way. A key learning of this research is that machine learning methods for non-invasive neural interfaces are not the best way to restore hand function for amputees. Hand function is best restored through the combining of machine learning and machine vision into a single NI based control framework. Additionally, this work is based in Mātauranga Māori with a western science approach. By so doing, amputees will be empowered, have increased independence and enjoy a better quality of life.

The scope of this work is limited to the consideration of hand function restoration for upper extremity amputees. In particular trans-radial amputees. Focus is given to non-invasive methods of control, therefore, investigations are limited to the electroencephalography (EEG) signal. Mimicking the human grasping system is a complex and difficult process, therefore, the attempts in this work to restore hand function only consider mechanisms and processes related directly to the motor control of the human hand.

year: 2019