Saturday, 29 September 2018

Mobile Phones on Sheep

A recent paper has been published by Zainab Al-Rubaye on Detection on Lameness in Sheep using wearable sensor technology (at the moment an Android phone)




Al-Rubaye Z., Al-Sherbaz A., McCormick W., Turner S. (2018) Sensor Data Classification for the Indication of Lameness in Sheep. In: Romdhani I., Shu L., Takahiro H., Zhou Z., Gordon T., Zeng D. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 252. Springer, Cham

Abstract
Lameness is a vital welfare issue in most sheep farming countries, including the UK. The pre-detection at the farm level could prevent the disease from becoming chronic. The development of wearable sensor technologies enables the idea of remotely monitoring the changes in animal movements which relate to lameness. In this study, 3D-acceleration, 3D-orientation, and 3D-linear acceleration sensor data were recorded at ten samples per second via the sensor attached to sheep neck collar. This research aimed at determining the best accuracy among various supervised machine learning techniques which can predict the early signs of lameness while the sheep are walking on a flat field. The most influencing predictors for lameness indication were also addressed here. The experimental results revealed that the Decision Tree classifier has the highest accuracy of 75.46%, and the orientation sensor data (angles) around the neck are the strongest predictors to differentiate among severely lame, mildly lame and sound classes of sheep.


More details available at:
 https://www.researchgate.net/publication/327865785_Sensor_Data_Classification_for_the_Indication_of_Lameness_in_Sheep_13th_International_Conference_CollaborateCom_2017_Edinburgh_UK_December_11-13_2017_Proceedings


Relate Links





All views and opinions are the author's and do not necessarily reflected those of any organisation they are associated with. Twitter: @scottturneruon

Monday, 24 September 2018

Changing Minds


A chapter on multitasking in Higher Education has recently been published in a new book Higher Education Computer Science by a member of the Computing team Liz Coulter-Smith.


Coulter-Smith L (2018) "Changing Minds: Multitasking in Lectures"  Higher Education Computer Science DOI: https://doi.org/10.1007/978-3-319-98590-9_1


Abstract
This chapter takes a multidisciplinary approach to multitasking. Media multitasking has, consequently, become a frequent topic amongst academics yet some remarkable new research reveals we may not be taking into full account the changes to our students’ ability to learn given the changes to their brains. The risks of multitasking to student achievement has been well researched yet many of the positive related developments in the neurosciences are less well known. This chapter reviews some of this research bringing together information foragingInformation foraging theory, cognitive control and confirmation bias as they relate to the multitasking Generation Z student in higher education. Some significant research findings are discussed including using laptops and similar devices in the classroom. A small survey underpins these discussions at the end of the chapter highlighting student perspectives on multitasking during lectures.



All views and opinions are the author's and do not necessarily reflected those of any organisation they are associated with. Twitter: @scottturneruon

Friday, 21 September 2018

Mining rules in e-commerce applications


Xue, James (2018) Mining association rules for admission control and service differentiation in e-commerce applications. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 1942-4795.

To read more go to https://doi.org/10.1002/widm.1241


Abstract


Workload demands in e‐commerce applications are very dynamic in nature, therefore it is essential for internet service providers to manage server resources effectively to maximize total revenue in server overloading situations. In this paper, a data mining technique is applied to a typical e‐commerce application model for identification of composite association rules that capture user navigation patterns. Two algorithms are then developed based on the derived rules for admission control, service differentiation, and priority scheduling. Our approach takes the following aspects into consideration: (a) only final purchase requests result in company revenue; (b) any other request can potentially lead to final purchase, depending upon the likelihood of the navigation sequence that starts from current request and leads to final purchase; (c) service differentiation and priority assignment are based on aggregated confidence and average support of the composite association rules. As identification of composite association rules and computation of confidence and support of the rules can be pre‐computed offline, the proposed approach incurs minimum performance overheads. The evaluation results suggest that the proposed approach is effective in terms of request management for revenue maximization.



All views and opinions are the author's and do not necessarily reflect those of any organisation they are associated with. Twitter: @scottturneruon

Saturday, 15 September 2018

Computing for Social Good 7: Importance of Student Volunteering

Volunteering, It is good for the students, communities and the University: Reflections on STEM volunteering

Invited Talk around reflections on student volunteering at the "Higher Education for the Development of Iraq Conference" 14-15th September 2018 London. The conference 
aimed at looking at the integration between the work of the Iraqi Ministry of Higher Education and Scientific Research and the development needs in Iraq; by identifying problems in all sectors and contributing to the creation of a knowledge economy. 
This talk fitted under "This section is about the problems and challenges face the Iraqi economy which can be resolved by higher education outcomes." https://www.farismedia.co.uk/read-more  

e economy.





All views and opinions are the author's and do not necessarily reflected those of any organisation they are associated with. Twitter: @scottturneruon

Tuesday, 4 September 2018

University of Northampton - teaching and researching Blockchain recognition

Taken from: University of Northampton recognised for being one of a handful of institutions teaching and researching Blockchain



The University of Northampton has been recognised as one of only a handful of Higher Education (HE) institutions worldwide which are teaching or carrying out Blockchain research.
Blockchain is a shared, replicated ledger that underpins technology such as cryptocurrency, but also sets out to provide the foundation for the next generation of transactional applications.
Blockchain analyst website Diar has included the University of Northampton in a list of just 28 HE providers that teach aspects of Blockchain and/or conduct research into it.
Northampton does both.
Postgraduate students on the MSc Computing course are taught elements of Blockchain, including a general introduction to the basic concepts, plus coding and programming techniques.
Meanwhile, various Northampton academics, led by Senior Lecturer in Education, Dr Cristina Devecchi,  have collaborated on a Blockchain project to help Syrian refugee children which has been promoted by the United Nations.
Dr Scott Turner, who teaches Blockchain on the MSc Computing course, has also delivered a talk with colleague Ali Al-Sherbaz about the subject to the British Computing Society.
The University’s Vice Chancellor, Professor Nick Petford, said: “It is good to see the work of the University of Northampton recognised as contributing to the academic and practical development of Blockchain.
“The technology offers a new way of looking at old problems with great potential to innovate across a wide range of our research activities from education and humanitarian aid to supply chain management.”

All views and opinions are the author's and do not necessarily reflected those of any organisation they are associated with. Twitter: @scottturneruon

Tuesday, 28 August 2018

Web VR with the Oculus DK1

A recent BSc Games Development project by Jack Taylor look at the use of older VR equipment options running on newer operating system, specifically Windows 10. Aim of this work was a lower cost system, for users, including educational use. In the second post below Jack discusses his work.



Web VR with the Oculus DK1
Jack Taylor

In my previous blog post, I covered the usage of older VR technology within development environments. This demonstrated the installation of the Oculus DK1 on Windows 10, as well as the it’s use with Unity 2017. As a follow up, this blog post will extend the uses of the Oculus DK1 within a Web VR development environment using AFrame and HTML. If you have not read the first blog post, please do so to follow the instructions on how to install and use your old Oculus headset on Windows 10. You can find that here: https://computingnorthampton.blogspot.com/2018/05/vr-on-cheap.html

PLEASE NOTE: When creating this project, I had to use Mozilla Firefox as other browsers lack support for certain platforms when using WebVR.
You will also need to have SteamVR running when testing WebVR scenes, else this will not work! If you have not yet read my other blog post, please do so to ensure that you have covered the installation of your Oculus DK1 headset!

Setting up your AFrame environment.






To start your development, you will first need an IDE. Any IDE is suitable for this process. I would recommend Sublime Text or Notepad++. Please ensure that your VR device is connected to your machine before starting this process.
To begin, you will first need to create an index file for your WebVR project. I recommend starting a nice empty folder, so you have a clean work environment. When creating the file, please make sure that you save it as a HTML file before you continue.
When you’ve created the file, you will need to start with a layout like mine. However, feel free to customise it as you wish.


Once you have saved the file and filled it with the contents as shown above, you will be able to start developing a web page!
Now, for WebVR to work as we would like it to, we need to import some global scripts which are provided by AFrame within the head tags of your file. You can find the script on the AFrame documentation here: https://aframe.io/docs/0.8.0/introduction/. Once you have inserted this script, your web page will support the AFrame API, which you can use to create your scenes.
To create a scene for your web page, you will need to add a couple of tags to between your and tags. The tags you will need to insert will create a scene for you, so you can start putting objects in to your scene. These tags are and .
Once completed, your file should look like this:



Now we have our scene set up, let’s start adding some surroundings to our scene.
For this example, I will be using some assets provided by my University. If you would like to explore the models available for AFrame scenes, you can view them on the AFrame documents here: https://aframe.io/docs/0.8.0/introduction/html-and-primitives.html.
Luckily with AFrame, you can specify different styles and sizes for the objects you add to your scene. For this example, I will be using , , , and elements. I will continue to post images of my example as we progress, so you can use them as a reference when creating a project, should you wish to do so.
Before I demonstrate the implementation of assets within our scene, I would recommend running your new file in a browser to make sure that everything is working as intended.
Your scene will be blank, and will look like this:



Notice the headset icon in the bottom right of your scene. That will trigger your VR headset and set your browser to run in Full Screen.
Okay, let’s start populating our scene by adding a few elements. First, we will start with a Sky which will brighten up our project a bit. For this example, here is the code I will be using:
Feel free to change the colour around to suit your project needs. Once added, save and run your file again to view your changes. You can do this as many times as you like.
Once saved, your project will look like this:


You can continue to add more elements to your scene as you would like. AFrame allows you to specify different properties for your objects, such as the position, colour, size, and many more.
Let’s add a few elements to this scene to populate it a bit.
Here is the code for the elements you should add next as part of this example:


 
value="UO" color="#111" position="0 1.8 -0.5"  align="center" width="2.6">



As you can see, the four new lines above have new properties, which will contribute to the way these objects behave in your scene. A full list of the API properties can be found on the documentation (https://aframe.io/docs/0.8.0/components/background.html). Save your file and run it within your browser.
Your scene should now look like this:
Feel free to try this out using your Virtual Reality headset.

-->
That just about covers the basics of using WebVR with your Oculus DK1 headset. I hope this article was helpful! Please feel free to suggest any changes to this post if you think that something is missing. Enjoy your experiments!

All views and opinions are the author's and do not necessarily reflected those of any organisation they are associated with. Twitter: @scottturneruon

Thursday, 23 August 2018

Visualising the Research 1: Visualising the authors

I love seeing data displayed visually; like a lot of people handling data one of the first thoughts that goes through my head is how can I 'look' at this. Recently, I have become interested in how particular groups of researcher worked together 
- can this be the visualised?
- how does the group work? Or rather can we get a sense of how the group works?
- Is there some insights that we can draw about the group from more quantitative approaches - if you like (and I do) social network analysis based on publications.

The particular group is a group of computing researchers based at the University of Northampton. The data comes from the University's repository - NECTAR. It is not expected that the process will reveal a highly nuanced analysis, lots of personal aspects important in research collaboration won't be picked up; but the goal is just to have a starting point.


Collecting the Data.
This is the easiest part, in this case, because the data comes from the repository we just need it in a form suited to the tools that follow. A two-stage process was followed
- Go to http://nectar.northampton.ac.uk/view/divisions/SSTCT.html the page linking to all the papers in the repository categorised as from the computing team;
- From there export the list into Reference Manager/.RIS format the tool for the next stage can take information in that format.


Starting the visualisation: coauthors
My tool of choice is VosViewer (http://www.vosviewer.com/). Once the software is running, find the create button and then load your .RIS file from the previous stage, I select to not include all groups just to visualise the main group (13 records were excluded). Some of the other settings including authors even if they were a co-author on one paper.
In this example we get the following:
Connections appear as straight lines and the maximum possible lines was set to show all the lines. It seems to show 'hubs' the larger the circle the more documents they have in the repository. The last thing I am going to do is save the network as a Pajek network file using the save button on the lefthand menu and select the option.


Putting some numbers to it!
Love the visuals but now I want to quantify the node (the authors) role in the network of co-authors. All of that is really saying can we get some new insights from looking at the network, as a social networking analysis task. My favourite tool for this is the widely used free and open-source Gephi (https://gephi.org/). 

Load in the Pajek formatted file saved in the last stage (it should have a .net extension). Once the file is loaded (in the options make sure is set to be an undirected graph), it usually puts the viewer into the Data Laboratory view, change the view to Overview. The graph may now appear as a single circle on the screen, now the fun can begin. Down the lefthand side of the screen there is a Tab marked Layout which brings-up a dropdown menu of different layouts have a play, select one and press run and see what they do.

Down the right side of the screen there a number of options. What we are going to focus on here, as an example, is the Edge Overview and the one option there Average Path Length; find the option and press run a screen will come up saying the three measures (see above) we will get, press ok, and then change the view to Data Laboratory. Below are some initial insights for these measures

  • Betweenness Centrality- sorting based on the one showed the 'hubs' that were presented in the first figure score highly. One aspect I wasn't expecting was two MSc Computing student who have published scored quite highly due to the papers they published linking authors who haven't collaborated previously
  • Closeness Centrality: Again the 'hubs' scored highly but also so did one of the MSc students.
  • Eccentricity: A low eccentricity score was seen for the hubs in general.


The insight I have found most interesting is the idea that MSc students publishing might have a positive knock-on effect on connecting staff researchers.




All views and opinions are the author's and do not necessarily reflected those of any organisation they are associated with. Twitter: @scottturneruon