What can we learn from the TFL?

London Tube

At Digifest 2017 , the closing keynote was from Lauren Seger Weinstein, chief data officer at Transport for London (TFL).

Lauren Sager Weinstein, head of analytics at Transport for London, has responsibility for the analysis of customer data, supporting operational and planning areas in delivery of services to TfL’s customers. She joined TfL in 2002, where she has held a variety of roles.

During her time at TfL, Lauren has worked on a number of projects: the establishment of TfL’s first long-term funding package for infrastructure investment; the successful delivery of the London 2012 Olympic and Paralympic Games by providing analysis on travel patterns; the launch of contactless payment card acceptance on the TfL network; and the creation of TfL’s ODX tool that provides a multi-modal picture of customer travel patterns.

In her talk she covered how TFL was gathering data about how users were using the transport network. Using unique wifi device data, oyster cards and other mechanisms, they were able to see how people were travelling and the routes they used.

TFL Poster

They were hoping to use the data to be able to improve the network and the user experience across the network.

There had been an article in Gizmodo too on the same subject, it also had more detail about the trial.

To have had your data collected in the trial, all you needed to have was your wifi switched on – then the various wifi hotspots around the Tube network would be able to pick up your phone (or tablet, or laptop, or whatever)’s unique MAC address that enables you to be identified.

The good news for the paranoid is that TfL appears to have gone out of its way to make sure everything is above board. In the documents that Giz UK has seen, it makes clear that it is only MAC data that’s collected (ie: they’re not monitoring the websites you visit) – and that this data is stored as encrypted hashes – so even if hackers could somehow break in and obtain the collected data, they wouldn’t be able to get any MAC address data.

So what can we learn from the big data testing for the London transport network and build into how (or even if) we can track learners and use that to support campus design, improve the use of learning spaces, social spaces and informal learning spaces.

One thing that was very apparent from TFL is that they ensure that privacy of their customers is core to their collection of data.

No personal identifiable data is used to track devices as they move across the TFL network. So they can track devices, but can’t identify the device or the individual holding the device.

Reaching out to their customers, they got some interesting feedback as reported in the Gizmodo article.

What’s interesting though is that the cache of documents contains the results of research that TfL commissioned from a company called 2CV aimed at analysing customer attitudes to tracking their data – which makes for interesting reading.

For example, it revealed that customers are much more okay about sharing data when they feel that they are making an “informed decision”, and that many people are “apprehensive” about mobile tracking, because it is so new. The sharing of location data in particular is “viewed differently” to other private information too.

“It is clear that communicating the technology and raising awareness of its use will be critical in driving acceptance of TfL using it”, the research notes. Apparently once people understand the benefits, they are much more accepting of it.

Any similar project across a university or college campus should consider the ethical and privacy implications of tracking students and how that data will be used.

So are you tracking technology across your campus? What did you do about the ethics or privacy issues?

Is there any space in the library?

In my previous blog post I asked the question

So do you already have such a system in place, does it do what it’s supposed to do, what would you do differently?

Using the power of the Twitter I received a few responses, one was from Ruth MacMullen.

The University of York Library has a page outlining availability of seating. When I checked the page you could see there was minimal seating available.

The University of York Library has a page outlining availability of seating.

The information is not live and is updated hourly.

What I didn’t realise when I wrote my previous blog post (and used an image of Costa) was that Google was also providing a similar information view on the University of York Library. It is this information which is used to inform the availability page.

Google was also providing similar information

I did wonder how Google was measuring the “busyness” of the place.

busy

“based on visits to this place”

So to compare I did look at how busy Google thought other university libraries were.

Down in Plymouth, at roughly a similar time, not so busy.

Plymouth

Up in Stirling, well not at all busy, for all we know the library is empty, but somehow I think not.

Stirling

As I said in my previous blog post was that historical data is useful in predicting future usage, but if you could combine it with live data as well, you might provide a better experience for learners.

I’m sorry that’s my seat!

I’m sorry that’s my seat!

As we start to reflect on the possibilities of the Intelligent Campus for Higher Education and Further Education, it is useful to lift our heads above the educational parapet and see what is happening in other sectors. When we see what others are doing we can learn from them and come up with new ideas and concepts.

One of the early thoughts we had as we looked over the Intelligent Campus was simple things such as informing learners about availability and capacity of learning spaces in the library (or even a coffee shop). The learners could be informed through a mobile app, or even on screens.

Virgin Trains at Euston have a system and a process that uses a visual indication of the availability of unbooked seats on their trains that is shown on the departure boards.

Virgin Trains at Euston have a system and a process that uses a visual indication of the availability of unbooked seats on their trains that is shown on the departure boards.

This is what many would call a dumb system as it doesn’t show actual availability, as the availability is based on the reserved seats, not on people actually sitting on those seats. Those who travel by train will know that not everyone who reserves a seat always sits in that seat on that train. Likewise there will be people sitting in the unreserved seats already (and the system won’t take that into account). With trains, the information is only needed for a short time, so these issues aren’t problematic. Using a similar system for, say a library computing booking system, could cause frustration for learners. If the library computer availability is based on bookings then you can imagine scenarios where learners could be frustrated.

One scenario, is a learner who accesses the board or app sees that all the computers are booked, assumes they are all being used and isn’t able to do their studying. The reality may be that thought the computers are booked, the learners who booked them have either finished early or didn’t turn up.

Another scenario, has the learner checking the availability from home, seeing that there are plenty of free (or non-booked) computers available, makes a journey to the campus and then finds that there is no availability as the computers are all being used by learners who popped into on the off-chance that the computers would be free.

You can see then that any system dependent on bookings or reservations is quite limited. If you could add to the system computers which are actually being used and this live data is added to the system then this will be more accurate in informing learners about availability. Though this is only useful for the current time, planning for the future availability is back to being dependent on bookings.

What could make this more useful is, if historical data was added to the system , so that as well as knowing the bookings, the learner is informed of when the space is busy and when it is more likely a computer will be available. Google has a similar concept if you search for a retail outlet.

Costa Coffee

This is also useful if you have banks of computers that aren’t available to be booked.

Any such system needs to be useful to learners, so in the first instance it can be advantageous to check if you have a problem in the first place that would need a solution. If there is demand for such a solution, ensure that you have the right data and any interpretation of that data is accurate. As with any new system review and evaluation should be undertaken to check that it’s all working and that it is useful to learners.

You may also want to consider the usefulness of the data to others in the organisation, obviously IT, but also estates (for cleaning access) and catering (for peaks in demand for coffee in the nearby coffee outlets).

So do you already have such a system in place, does it do what it’s supposed to do, what would you do differently?

Hijacked vending machines cause chaos

vending machine

A university had been receiving an increasing number of complaints from students across campus about slow or inaccessible network connectivity.

It turned out that hijacked vending machines (and 5,000) other Internet of Things (IoT) devices attacked the university network and slowed it right down.

The investigation into the campus network slow down found problems with five thousand systems.

The firewall analysis identified over 5,000 discrete systems making hundreds of DNS lookups every 15 minutes. Of these, nearly all systems were found to be living on the segment of the network dedicated to our IoT infrastructure.

The university had made a large investment into an intelligent campus for management and efficiency.

With a massive campus to monitor and manage, everything from light bulbs to vending machines had been connected to the network for ease of management and improved efficiencies.

The problem was narrowed down a spreading botnet.

This botnet spread from device to device by brute forcing default and weak passwords. Once the password was known, the malware had full control of the device and would check in with command infrastructure for updates and change the device’s password – locking us out of the 5,000 systems.

The case study does talk about the final solution, but also importantly the lessons learned.

Don’t keep all your eggs in one basket; create separate network zones for IoT systems; air-gap them from other critical networks where possible.

The Internet of Things could provide powerful benefits to a university or college, but as stories such as these show us, sometimes we also need to balance those benefits with am understanding of the potential risks, but also what needs to be done to mitigate those risks.

How are you protecting your network and IoT devices?

Download PDF Case Study

Via Verizon

What are our new priorities, and what next? #codesign17

There has been plenty of hype over artificial intelligence and the internet of things. We believe it may be time to put aside the cynicism that this kind of hype generates and look seriously at how we can take advantage of these emerging technologies to improve the student experience, research and the management of our campuses.

vine-1010002_1920

We decided that the three ideas were use-cases which would be enabled if we could develop the basic data infrastructure for the intelligent campus.

However that is a big, long-term development so we decided that our immediate goal should be to analyse in more depth the potential use-cases as well as the technical, ethical and business implications of this approach. Doing this will give us a better idea of where best to get started on developing the data infrastructure for the intelligent campus.

Read our blog about the results of the voting stage.

We will be sharing the initial results of our exploration in May. Keep an eye on the Jisc blogfor announcements.

If you want to comment on the ideas, tweet using #codesign17 or email james.clay@jisc.ac.uk.

Join us for a live Tweetchat 20th January 2017

Join us for a live tweet chat on the subject of the intelligent campus by using #codesign17 on Friday 20 January 12:30-13:30.

There has been plenty of hype over artificial intelligence and the internet of things. We believe it may be time to put aside the cynicism that this kind of hype generates and look seriously at how we can take advantage of these emerging technologies to improve the student experience, research and the management of our campuses.

Three ideas came out of the discussion in relation to the Intelligent Campus.

I didn’t know I needed to ask…

Arriving at a new institution is a disorientating experience. As students walk around the university or college campus they are faced with problems that need to be resolved in order to help them settle, provide a satisfying experience and even help them on their learning journey.

Could Jisc help build the tools and practices an institution would need to use to gather, organise and push this data to student’s smartphones as well as exploring novel user interfaces such as chatbots?

If the walls could talk

If the spaces we use for teaching and learning could speak to us, what would they say?

There is an institutional memory within those walls that is inaccessible and lost every time the learners leave the room. The room doesn’t remember what worked well or what could have been better. The spaces, if they could store experiences and feedback, would know what was the ideal environment for different learning activities.

Could Jisc help build the tools required to make the gathering and analysis of that data easier as well as exploring how to best act upon the insights produced to make changes?

Looking for the narrative

As we start to use a range of tracking technologies, smart cards, beacons, sensors we are facing a deluge of data in the use of buildings, spaces and equipment across a college or university campus. We are faced with a breadth and depth of data which can be challenging to use effectively and have greatest impact.

Is there any benefit in Jisc providing a platform to help gather this data from a range of systems in a standard format that makes it easier to analyse and act upon? Would it be useful to have a national view over this data? Would that enable us to find new patterns that could help us discover the story behind the data, to make appropriate interventions and improve the use of our campuses, buildings and rooms?

Could this be also an experimental platform for any interested researchers or developers. A combination of thingful.net and data.gov.uk but with UK HE and FE data perhaps?

Next steps

We are asking people to express support for any of the ideas they particularly like.

If you like any of these ideas, please register your support using our form. All expressions of support will be publicly visible. The form will be open until 30 January 2017.

Fill in the form.

See a spreadsheet of the results so far.

Please visit our co-design 2016-17 page to find out more. The feedback period closes on 30th January and we will announce the ideas we are exploring in early February.

Why connecting all the world’s robots will drive 2017’s top technology trends

By Tom Garner, University of Portsmouth

If you want to make predictions for the future, you need to find the trajectory of events in the past. So to work out what shape digital technology will likely take next year, we should look back to the major developments of 2016. And the past year’s developments point to a 2017 shaped by the next phase of virtual and augmented reality, the emergence of an internet for artificial intelligence and the creation of personalised digital assistants that follow us across devices.

Virtual world

One technology in particular has dominated the news throughout the year and made the birthday wish-lists of children and adults alike: virtual reality. VR began to bloom commercially in 2016 – with HTC, Oculus (owned by Facebook) and PlayStation all releasing their latest headsets. But 2017 will almost certainly be a pivotal year for VR, given its rather precarious position on the “hype cycle”.

This is a research methodology for predicting the commercial dominance of an emerging technology as it matures and goes through periods of increasing hype, sudden disillusionment and eventual success. Presently VR is on the precipice of the “peak of inflated expectations”, where the hype exceeds the reality and quality comes second to novelty.

In the hype cycle model, the peak of excitement is followed by an inevitable fall (the “trough of disillusionment”), as consumers realise the gap between what they expect and what they actually get. Here is where opinion is divided on VR. For some this will be a gentle dip, while for others the drop will be a portent to collapse.

The big question splitting these opinions is whether the consumer reaction to the VR games and applications currently being released will be the wrath of disillusionment or the mercy of patience. The more convincing assertion is that mobile phone-based VR platforms (with their greater ease of use, lower cost and wider range of games and applications) will help stabilise VR throughout 2017.

More than a gaming platform?
Shutterstock

Augmented success

But stability is not the same as success. VR also has the problem that its consumer appeal is primarily recreational, limited largely to games and 360-degree videos. So far it has had relatively little impact on social or functional applications such as providing an interface for social media.

The same cannot be said for its more versatile but currently less well-known cousin, augmented reality. AR – which involves overlaying images of the real world with additional graphics or information – has enjoyed much success of late as a gaming platform, particularly thanks to the release of Pokémon Go.

Yet AR functionality already goes beyond games, and it is an ideal delivery mechanism for limitless forms of digital information. Concepts include heads-up displays attached to cyclists’ helmets that provide them with a 360-degree field of view and also alert them to potential dangers by tracking overtaking vehicles. But also applications such as visual overlays that can virtually redecorate your entire home without a single lick of paint.

The real future of AR however is in it’s potential to give us a new and improved means of accessing content and services we already cannot do without. As Microsoft’s HoloLens and Google Glass have alluded to, 2017 could see us using AR to check our emails, posting on Facebook and discovering the best route to our meeting place across town, with all content delivered straight to our eyes. Not a single aversion of our gaze or break in our stride required.

Current investment in the sector is prioritising advances in relevant underlying technologies such as depth-sensing camera lenses and physical environment mapping systems. This suggests that the industry is readying hardware to ensure these exciting ideas can materialise. It doesn’t mean that all ambitions of AR will be realised in 2017, but they are tantalising possibilities, depending on whether the underlying technology can make them a reality.

Intelligent connection.
Shutterstock

Internet of Robots

The other area where we are likely to see some fascinating research developments moving into commercial applications is artificial intelligence and machine learning. And the application most likely to dominate 2017 is the Internet of Things, the connection of millions of ordinary devices, from cameras to kettles, to the internet.

The concept of the Internet of Things champions our seeming desire for constant connection, with the physical objects we use everyday all linked together in a glorious (or terrifying) chain. 2017 could be the year we’ll all be telling our telling our barista coffee machine at home to prepare us a chocolate fudge Café Cubano from five miles away, using a bespoke interface in our car as we’re driving home.

Or perhaps not. But this ethos of interconnectivity is already reaching the realm of artificial intelligence with Cloud Robotics. These systems allow robots that have been optimised for different tasks to work on specific problems individually, but to pass solutions between each other.

The robots use the cloud to share the data, enabling it to be analysed by any other robot or intelligence system also connected to the same network. One robot teaches something to another, who in turn develops it and passes it forward in a collaborative effort that could massively increase the learning potential and connectivity of machines.

Personal digital assistants

All of these trends comes together for our final 2017 prediction: the rise of humanised digital technology in the form of intelligent personal assistants. These are essentially human-emulating data hubs. They use advances in artificial intelligence to capture and interpret our data, the Internet of Things to operate everything around us, and the advances in augmented reality to project themselves convincingly into our mobile world.

This will provide a single, naturalistic interface between us and our digitally connected universe. It is the next iterative step for the likes of Siri, Cortana and Alexa: an intelligent assistant able to travel with us wherever we go, across every device we use, to assist us in nearly every aspect of our lives.

Tom Garner, Research Fellow, School of Creative Technologies, University of Portsmouth

This article was originally published on The Conversation. Read the original article.

The Conversation

Intelligent Campus: Next stage of Co-Design 2017

museum

Late last year we kicked off a consultation to identify what big new ideas Jisc should focus on once we have completed our current R&D projects. That consultation focused on 6 possible challenges:

  • What does the imminent arrival of the intelligent campus mean for universities and colleges?
  • What should the next generation of digital learning environments do?
  • What should a next-generation research environment look like?
  • Which skills do people need to prepare for research practice now and in the future?
  • What would truly digital apprenticeships look like?
  • How can we use data to improve teaching and learning?

Thanks to all the interesting discussion around these challenges we have come up with ideas for how Jisc could help with five of them. We now need help from Jisc members and other experts to decide which of those ideas would be most valuable for us to pursue. So we are asking people to express support for any of the ideas they particularly like. Please visit our co-design 2016-17 page to find out more. If you have any feedback or suggestions that don’t fit in our feedback form then please contact the relevant challenge lead or Andy McGregor as we are keen to hear all types of feedback. The feedback period closes on 30th January and we will announce the ideas we are exploring in early February.

Three ideas came out of the discussion in relation to the Intelligent Campus.

I didn’t know I needed to ask…

Arriving at a new institution is a disorientating experience. As students walk around the university or college campus they are faced with problems that need to be resolved in order to help them settle, provide a satisfying experience and even help them on their learning journey.

There are the known problems, such as where is my next lesson? What books would be useful for this topic? When is my tutor free for a quick chat on assignment? Even simple questions could result in a complicated route to multiple online systems. Imagine asking the question, where and when is my next lecture, what resources are available and are there any relevant books in the library on this subject?

There are also the unknown problems, these are the kinds of problems that learners don’t even know they have and haven’t thought to ask? Could the university or college push information and notifications to learners based on where the learner is on campus, when the learner is on campus, and how far the learner is on their learning journey?

Could Jisc help build the tools and practices an institution would need to use to gather, organise and push this data to student’s smartphones as well as exploring novel user interfaces such as chatbots?

If the walls could talk

If the spaces we use for teaching and learning could speak to us, what would they say?

The spaces across colleges and universities are core to teaching and learning. Are we using them effectively to enhance and enrich the learning journey? Does the environment in which we learn have any or a significant impact on that journey?

There is an institutional memory within those walls that is inaccessible and lost every time the learners leave the room. The room doesn’t remember what worked well or what could have been better. The spaces, if they could store experiences and feedback, would know what was the ideal environment for different learning activities.

Could we use data gathered from teachers and students, as well as space usage, to inform and improve teaching and learning?

Could Jisc help build the tools required to make the gathering and analysis of that data easier as well as exploring how to best act upon the insights produced to make changes?

Looking for the narrative

As we start to use a range of tracking technologies, smart cards, beacons, sensors we are facing a deluge of data in the use of buildings, spaces and equipment across a college or university campus. We are faced with a breadth and depth of data which can be challenging to use effectively and have greatest impact.

Interpreting the data is fraught with challenges and difficulties, as well as potential ethical and legal issues. However this wealth of data does offer the potential to deliver more satisfying experiences for students and staff as well as ensuring the campus is used as effectively as possible.

Is there any benefit in Jisc providing a platform to help gather this data from a range of systems in a standard format that makes it easier to analyse and act upon? Would it be useful to have a national view over this data? Would that enable us to find new patterns that could help us discover the story behind the data, to make appropriate interventions and improve the use of our campuses, buildings and rooms?

Could this be also an experimental platform for any interested researchers or developers. A combination of thingful.net and data.gov.uk but with UK HE and FE data perhaps?

Next steps

We are asking people to express support for any of the ideas they particularly like.

If you like any of these ideas, please register your support using our form. All expressions of support will be publicly visible. The form will be open until 30 January 2017.

Fill in the form.

See a spreadsheet of the results so far.

Please visit our co-design 2016-17 page to find out more. The feedback period closes on 30th January and we will announce the ideas we are exploring in early February.

Building the future

Last month I had some interesting conversations with Michael Burns at Glasgow on the subject of the Intelligent Campus (or the Smart Campus). We discussed many of the issues that we have been looking at as part of the #codesgin16 challenge and what is happening in Glasgow.

Smart-Campus-image-768x597

The first question I asked Michael was, what do you understand by the concept of the smart campus?

The university has committed to spend around £1Bn on the creation of a new consolidated campus on its Gilmorehill site in the west of the city. Many other universities are also committing similar amounts to their own projects. This huge investment in educational  infrastructure comes at a time where there is enormous Government and industry interest in the development of ‘smart’  urban solutions. It makes sense for us to identify where the opportunities for collaboration lie within this.

Student satisfaction underpins the university’s business case in the creation of the new campus  – and ‘smart’ technology has the potential to strengthen this – whether it in the management of a built environment designed for an end-user experience which is accessible, comfortable, connected, convenient, secure and welcoming through to the interests of a research community and the engagement of new industry partners. In this sense ‘smart’ campus becomes more than an efficient estate  – it also provides a strong digital experience where the University is more aware of , and sensitive to, the needs of its stakeholders – students, faculty, support staff, suppliers and the host community with whom it shares the city.

Our early approach to smart campus is weighted towards energy, construction innovation and sensors, however these are not an end in themselves. They are part of a larger, more complex, conversation about we use these technologies to design, develop and deliver a campus attuned to the needs of its users.

Do you think that there are realistic opportunities to enhance learning and research by using artificial intelligence and the internet of things?

These are already established parts of research in many institutions worldwide. Overall, it seems unlikely that automation of core cognitive processes is a viable route. Instead, using AI and the like to augment human activity is the way to go.

With regard to learning, more work has to be done with staff and students, to establish solid guidelines and infrastructures for system design—but again there are people exploring research possibilities already, including groups in U. Glasgow. We are exploring infrastructures, such as digital ledgers, that can support a range of apps, analyses and decision-making processes.

We’re looking at IoT as it can help shape the creation of a larger digital district around the new campus — enhancing the student experience further by facilitating greater access to a range of complementary city services e.g. leisure, transport, social activities. This is looking as it could be delivered as a partnership with the City Council, Scottish government and the private sector.

It is easy to imagine using this data to ensure our rooms and facilities are managed effectively, but could we go further and monitor environmental conditions in learning spaces or even, using facial recognition software, student reactions during learning so that we can continually refine the learning experience?

Clearly, basic management of the built environment is easier to do if the data collected is inherently anonymous, e.g. environmental conditions or overall numbers of people, and in that case we are likely to benefit from the application of existing industry solutions – ours is a live project. Many powerful (but often indirect) benefits can be gained this way, e.g. allowing the university to spend the money it would have used on wasted energy on services for staff and students.

When one goes beyond that, then such systems have to be designed in ways that involve the people who would be sharing such data with the university. Perhaps the starting point should not then be that the university monitors staff and students, but that staff and students share data with the university if they wish. Allied with this, a simplistic yes/no agreement is inadequate. We see the need for controls as well as individual benefits for the people (staff and students) who choose to share such information, e.g. personalised apps and services, to support campus life. People may share such data from altruism or campus community spirit, but direct benefit can take us further.

What constraints, if any, should institutions have placed on them when it comes to tracking data?

Constraints already exist, and should exist, based on a body of law and established procedures—ethical and technical. This is only one part of the high-trust relationship between the university and staff/students. Another is its maintenance and development, through appropriate forms of procedural and operational transparency, and engagement of the campus community in design processes. 

As we continue to look into this area, we are interested in seeing what other people are working on.

It’s all about my screens

From the Turing Lecture Series

The remarkable adoption of the smart phone globally has completely revolutionised the way we play, work, and live. New applications and services are being created on a nearly daily basis giving us access to global information at the press of a search or a question to Siri.

We now have this exact same power on our wrists as we had on personal computers just a few short years ago. We cannot even imagine a world where we are unconnected and out of touch.

In near parallel, we are living in a world of big data that gets bigger with each passing day. People and machines are generating incredible amounts of content that can be accessed at any time, courtesy of cheap, connected storage.

Thus lies the crux of our information challenge in the future. Data is everywhere. It’s overwhelming. How does one find a true and accurate answer in a sea of knowledge, opinion, disinformation and pure speculation?

Tools like machine learning, natural language processing, and artificial intelligence are all billed as ways of mining information to cut through the clutter and deliver answers.

Those answers need to be relevant, contextual, timely, and most importantly, personal on whatever device is handy and appropriate.

This lecture will explore our future in this hyperconnected environment, and how our lives will seamlessly drift into a work-life blur based on a “dayflow” of activity.

The Lecturer will describe the digital trends which are driving this from both a consumer perspective as well as from the service provider and will paint a picture of what this means for our 21st century lives.