Emoting a wider range of expressions in Teams

Many of us are spending a lot of time in Teams meetings. One challenge of remote working is the reduced ability to express, and pick up, subtle body language and facial cues, which can contribute to difficulty communicating – even before broadband connection comes into play.

Microsoft launched Reactions in Teams in December, which allows us to show a reaction while someone else is talking.

This is great, and people in meetings I’ve been in have found it really helpful. However, there are currently very limited options to emote. We can either like (thumbs up), love (heart), clap or laugh. Or put our hand up.

Great!…but we can’t use it to express different emotions. In particular, all the reactions are positive. This may contribute to pleasant team meetings – but risks contributing to ‘groupthink‘. The ability to convey uncertainty, or dissatisfaction, or frustration, are important social signals often communicated through subtle facial cues, which on a Teams call may be impossible to spot. If I’m not feeling comfortable for some reason in a Teams call, my only options are to speak out verbally, or keep schtumm, or use the comments (which a speaker may not see).

I was recently in an excellent session on Challenging conversations – having a visual way to challenge statements, may add to verbal intervention as a way to signal that something is not OK.

Zoom and Slack have a much wider range. Taking Zoom as an example – more, still quite positive, but with the ability to thumbs down or say ‘No’:

So how we do extend the range of emotional expression in Teams? Microsoft say they’re working on extending the range, but there isn’t a timescale.

Someone has created a technical solution, but it needs to be set up by sysadmins in the organisation (example).

I’ve come across other ways for signalling emotions, including non technical – for example, some teaching staff encourage students to use their Teams/Zoom background, or even their clothes, to signal how they’re feeling (red or amber for different shades of ‘I’ve got some concerns’).

From a discussion at a team meeting I decided to try and solve this problem, using Snap filters. The brief here was to create a filter that allowed a wider range of emotes, presented in the same style as the existing Teams reactions, and in particular to plug the gaps in current reactions around expressing uncertainty or concern.

I present – the Emoji Board! Use the link to access, or scan the following with Snapchat:

Using this with Snap camera allows the following emotes, presented in the same style as Teams reactions (appear on screen for 3 seconds, centred, transparent background)

😂lol/hilarious (emphasis)


👎 dislike

😱 shock




The filter should be usable on mobile phones but is optimised for use with Teams (or Zoom) on a laptop. To use, it click on the sides of the Snap camera screen to pop out the emotes.

Screen press locations

–originally posted on Vincent’s blog

International Summer School Programme on Artificial Intelligence

post by Edwina Abam (2019 cohort)


The summer school programme I enrolled on during this year’s summer was the 3rd edition of the International Summer School Programme on Artificial Intelligence with the theme Artificial Intelligence from Deep Learning to Data Analytics (AI-DLDA 2020).

The organisers of the program were the University of Udine, Italy, in partnership with Digital Innovation Hub Udine, Italian Association of Computer Vision Pattern Recognition and Machine Learning (CVPL), Artificial Intelligence and Intelligent Systems National Lab, AREA Science Park and District of Digital Technologies ICT regional cluster (DITEDI).

Usually, the AI-DLDA summer school program is held in Udine, Italy, however, following the development of the COVID-19 situation, this year’s edition of the AI-DLDA summer school program was held totally online via an educational platform and it lasted for 5 days starting from Monday 29th June until Friday 3 July 2020. There were about 32 PhD students from over 8 different countries participating in the summer school as well as masters students, researchers from across the world and several industry practitioners from Italian industries and Ministers.

School Structure

The school program was organised and structured into four intensive teaching days with keynote lectures in the morning sessions and practical workshops in the afternoon sessions. The keynote lectures were delivered by 8 different international speakers from top universities and high profile organisations. Both lectures and lab workshop sessions were delivered via a dedicated online classroom.

Key Note Lectures and Workshops

Day 1, Lecture 1: The first keynote lecture delivered was on the theme, Cyber Security and Deep Fake Technology: Current Trends and Perspectives

Deep Fake Technology are multimedia contents which are created or synthetically altered using machine learning generative models. These synthetically derived multimedia contents are popularly termed as ’Deep Fakes’. It was stated that with the current rise in ’Deep Fakes’ and synthetic media content, the historical belief that images, video and audio are reliable records of reality is no longer tenable. The image in figure 1 below shows an example of Deep Fake phenomenon.

The research on Deep Fake technology shows that the deep fake phenomenon is growing rapidly online with the number of fake videos doubling over the past year. It is reported that the increase in deep fakes is sponsored by the growing ubiquity of tools and services that have reduced the barrier and enabled novices to create deep fakes. The machine learning models used in creating or modifying such multimedia content are Generative Adversarial Fusion Networks (GANs). Variants of the techniques include StarGANs and StyleGANs.

Figure 1: Deep Fake Images

The speakers presented their own work which focused on detecting deep fakes by analyzing convolutional traces [5]. In their work they focused on the analysing images of human faces,by trying to detect convolutional traces hidden in those images: a sort of fingerprint left throughout the image generation process. They propose a new Deep fake detection technique based on the Expectation Maximization algorithm. Their method outperformed current methods and proved to be effective in detecting fake images of human faces generated by recent GAN architectures.

This lecture was really insightful for me because I got the opportunity to learn about Generative Adversarial Networks and to understand their architectures and applications in real-world directly from leading researchers.

Day 1, Lecture 2: Petia Radeva from the University of Barcelona gave a lecture on Food Recognition The presentation discussed Uncertainty modeling for food analysis within end-to-end framework. They treated the food recognition problem as a Multi-Task Learning (MTL) problem as identifying foods automatically from different cuisines across the world is challenging due to the problem of uncertainty. The MTL Learning problem is shown in figure 2 below. The presentation introduced aleatoric uncertainty modelling to address the problem of uncertainty and to make the food image recognition model smarter [2].

Figure 2: Food Image Recognition Problem

Day 1, Lecture 3: The final keynote lecture on day 1 focused on Robotics, on the topic: Learning Vision-based, Agile Drone Flight: from Frames to Event Cameras which was delivered by Davide Scaramuzza from University of Zurich.

He presented on several cutting edge research in the field of robotics including Real time, Onboard Computer Vision and Control for Autonomous, Agile Drone Flight [3]. Figure 3 below shows autonomous drone racing from a single flight demonstration.

Figure 3: Autonomous Drone Racing

The presentation also involved an update of their curent research on the open challenges of Computer vision, arguing that the past 60 years of research have been devoted to frame based cameras, which arguably are not good enough. Therefore, proposing event -based cameras as a more efficient and effective alternative as they do not suffer from the problems faced by frame based cameras. [4]

Day 1, Workshop Labs: During the first workshop we had practical introduction to the Pytorch Deep Learning Framework and Google Colab Environment. This was led by Dott. Lorenzo Baraldi from University of Modena and Reggio Emilia.

Day 2, Lecture 1: Prof. Di Stefano, gave a talk on Scene perception and Unlabelled data using Deep Convolutional Neural Networks. His lecture focused on depth estimation by stereo vision and the performance of computer vision models against the bench marked Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) data set. He also discussed novel advancements in the methods used in solving computer vision problems such as Monocular Depth Estimation problem, proposing that this can be solved via Transfer learning. [10].

Day 2, Lecture 2: In addition, Prof. Cavallaro from Queen Mary University of London delivered a lecture on Robust and privacy-preserving multi-modal learning with body cameras.

Lab2 – Part I: Sequence understanding and generation still led by (Dott. Lorenzo Baraldi, University of Modena and Reggio Emilia)

Lab2 – Part II: Focused on Deep reinforcement learning for control (Dott. Matteo Dunnhofer, University of Udine)

Day 3, Lecture 1 keynote lecture focused on Self Supervision Self-supervised Learning: Getting More for Less Out of your CNNs by Prof. Badganov from University of Florence. In his lecture he discussed self-supervised representation learning and self-supervision for niche problems [6].

Day 3 lecture 2 was done by keynote speaker, Prof. Samek from Fraunhofer Heinrich Hertz Institute on a hot topic in the field of Artificial Intelligence on Explainable AI: Methods, Applications and Extensions.

The lecture covered an overview of current AI explanation methods and examples of real-world applications of the methods. We learnt from the lecture that AI explanation methods can be divided into four categories namely perturbation based methods, Function based methods, Surrogate based methods and Structure based methods. We learnt that structure-based methods such as Layer-wise Relevance Propagation (LRP) [1] and Deep Taylor Decomposition [7] are to be preferred over function-based methods as they are computationally fast and do not suffer the problems of the other types of methods. Figure 4 shows details of the layer-wise decomposition technique.

Figure 4: Layer-wise Relevance Propagation

Overall, it was concluded that the decision functions of machine learning algorithms are often complex and analyzing them can be difficult. Nevertheless, levering the model’s structure can simplify the explanation problem. [9].

Lab3 – Part I: Lab 3 covered Going Beyond Convolutional Neural Networks for Computer Vision led by Niki Martinel and Rita Pucci from University of Udine

Lab3 – Part II: Going Beyond Convolutional Neural Networks for Computer Vision (Dott. Niki Martinel and Dott.ssa Rita Pucci, University of Udine)

Day 4: The final keynote lecture was done by Prof. Frontoni on Human Behaviour Analysis. This talk concentrated on the study of human behaviours specifically Deep Understanding of shopper behaviours and interactions using computer vision in the retail environment [8]. The presentation showed experiments conducted using different shopping data sets for tackling different retail problems including user interaction classification, person re-identification, weight estimation and human trajectory prediction using multiple store data sets.

The second part of the morning section on Day 4 was open for PhD students to present their research works to participants on the program.

Lab4 Part I: Machine and Deep Learning for Natural Language Processing(Dott. Giuseppe Serra and Dott.ssa Beatrice Portelli , University of Udine)

Lab4 – Part II: Machine and Deep Learning for Natural Language Processing (Dott. Giuseppe Serra and Dott.ssa Beatrice Portelli , University of Udine)

Concluding Remarks

The summer school programme offered us the benefit of interacting directly with world leaders in Artificial Intelligence. The insightful presentations from leading AI experts updated us about the most recent advances in the area of Artificial Intelligence, ranging from deep learning to data analytics right from the comfort of our homes.

The keynote lectures from world leaders provided an in-depth analysis of the state-of-the-art research and covered a large spectrum of current research activities and industrial applications dealing with big data, computer vision, human-computer interaction, robotics, cybersecurity in deep learning and artificial intelligence. Overall, the summer school program was an enlightening and enjoyable learning experience.


  • Sebastian Bach, Alexander Binder, Gr´egoire Montavon, Frederick Klauschen, Klaus-Robert Mu¨ller, and Wojciech Samek. On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PloS one, 10(7):e0130140, 2015.
  • Marc Bolan˜os, Marc Valdivia, and Petia Radeva. Where and what am i eating? image-based food menu recognition. In European Conference on Computer Vision, pages 590–605. Springer, 2018.
  • Davide Falanga, Kevin Kleber, Stefano Mintchev, Dario Floreano, and Davide Scaramuzza. The foldable drone: A morphing quadrotor that can squeeze and fly. IEEE Robotics and Automation Letters, 4(2):209–216, 2018.
  • Guillermo Gallego, Tobi Delbruck, Garrick Orchard, Chiara Bartolozzi, Brian Taba, Andrea Censi, Stefan Leutenegger, Andrew Davison, J¨org Conradt, Kostas Daniilidis, et al. Event-based vision: A survey. arXiv preprint arXiv:1904.08405, 2019.
  • Luca Guarnera, Oliver Giudice, and Sebastiano Battiato. Deepfake detection by analyzing convolutional traces. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 666–667, 2020.
  • Xialei Liu, Joost Van De Weijer, and Andrew D Bagdanov. Exploiting unlabeled data in cnns by self-supervised learning to rank. IEEE transactions on pattern analysis and machine intelligence, 41(8):1862–1878, 2019.
  • Gr´egoire Montavon, Sebastian Lapuschkin, Alexander Binder, Wojciech Samek, and Klaus-Robert Mu¨ller. Explaining nonlinear classification decisions with deep taylor decomposition. Pattern Recognition, 65:211–222, 2017.
  • Marina Paolanti, Rocco Pietrini, Adriano Mancini, Emanuele Frontoni, and Primo Zingaretti. Deep understanding of shopper behaviours and interactions using rgb-d vision. Machine Vision and Applications, 31(7):1–21, 2020.
  • Wojciech Samek, Gr´egoire Montavon, Sebastian Lapuschkin, Christopher J Anders, and Klaus-Robert Mu¨ller. Toward interpretable machine learning: Transparent deep neural networks and beyond. arXiv preprint arXiv:2003.07631, 2020.
  • Alessio Tonioni, Matteo Poggi, Stefano Mattoccia, and Luigi Di Stefano. Unsupervised adaptation for deep stereo. In Proceedings of the IEEE International Conference on Computer Vision, pages 1605–1613, 2017.

Detecting Fake Aerial Imagery – Call for participants

post by Matthew Yates (2018 cohort)

Hello everyone! I’m a 3rd year Horizon CDT PhD student partnered with the Defence Science and Technology Laboratory (Dstl). My PhD project is about the detection of deep learning generated aerial images, with the final goal of improving current detection models.

For this study, I am looking for participants to take part in my short online study on detecting fake aerial images. We have used Generative Adversarial Networks (GANs) to create these.

I am looking for participants from all backgrounds, as well as those who have specific experience in dealing with either Earth Observation Data (e.g. aerial imagery, satellite images) or GAN-generated images.

Purpose: To assess the difficulty in the task of distinguishing GAN-generated fake images from real aerial photos of rural and urban environments.  This is part of a larger PhD project looking at the generation and detection of fake earth observation data.

Who can participate? This is open to anyone who would like to take part, although the involvement of people with experience dealing with related image data (e.g. satellite images, GAN images) is of particular interest.

Commitment: The study should take between 5-15 minutes to complete and is hosted online on pavlovia.org

How to participate? Read through this Information sheet and follow the link to the study at the end.

Link to study:  Detecting Fake Aerial Imagery

For any additional information or queries please feel free to contact me: matthew.yates1@nottingham.ac.uk

Thanks for your time,

Matthew Yates

Lessons I learned from my internship

post by Harriet Cameron (2018 cohort)

Hi everyone! I hope you are all doing well, staying safe, and findings ways to care for yourself and your support networks during the apocalypse. I am going to talk a little bit about my internship that I completed in June 2020. I offer three (+1) lessons that I have learned throughout, in the hopes that they will be useful for readers still deciding when, where, and how to do their internships.

My internship was undertaken with my industry partner – the Nottingham Contemporary [4] – part-time over nine months. Starting in September 2019, I joined the small team working on the CAMPUS study programme [5]. I worked one day a week three times a month, and four days a week once a month. CAMPUS was designed as a city-wide programme aiming to bring together folks with a wealth of different backgrounds and perspectives to discuss critical pedagogies each month in venues and contexts across Nottingham. We organised speakers from all over the world to come to Nottingham and each deliver a three-day workshop to a group of 19 participants (who attended all seven workshops offered). We had seven amazing speakers planned, including Quinsy Gario [3], Gurminder K Bhambra [1], and Céline Condorelli [2].

CAMPUS offered a wide variety of experiences, stemming from both the opportunities it presented and the barriers and problems that arose throughout the nine months. Each month I was able to interact with the CAMPUS cohort, who ranged from curators, to lawyers, to artists, to unionists etc.; in order to swap and discuss our perspectives and life experiences. Each month I also met one of the seven faculty members co-facilitating the workshops and heard from their expertise. In fact, I would argue that the greatest part of this internship for me was precisely this opportunity to interrogate different critical pedagogies; some that I had explored before, some that I hadn’t, and none that I had explored in as much depth with as much passion as demonstrated by the speakers. One in particular that really sticks out to me was Gurminder K Bhambra’s talk about de-colonialising the university. After listening to her public talk and spending three days delving deeper into the concepts she brought up, I ended up completely re-evaluating everything I knew about and had experienced during my own journey through education. Quinsy Gario’s week was much the same, taking us through his experiences in art and activism and inspiring me to ask uncomfortable questions of my own life and cultures.

As well as offering opportunities, these amazing interactions came with their own challenges as well. It is easy to stop critically questioning and reflecting when you find your corner of academia to hunker down in – when you find the place that reflects the beliefs and values you brought in with you and are often, at least in my case, representative of the opinions of much of your wider support group outside the Ivory Tower. This isn’t some great fault of the human experience, I would personally attribute it to a kind of neural exhaustion – life and work (read: capitalism) takes up so much of our capacity that it’s hard to find space to ask hard questions and time to introspect and reflect comprehensively on the things you learn and see. So suddenly finding yourself surrounded by brilliant minds, with both the time and space to delve deep into topics that were at times, admittedly, uncomfortable to discuss, was hard but incredibly valuable.

The first lesson I learned and offer out to you, then, is this:

Be uncomfortable. How many times in your life are you offered three months to try something completely new? Take the opportunity to do something you haven’t done before, to try out new skills and ask new questions and meet new people. There is value in leaving your comfort zone.

Nine months is a long time to be included with an organisation or involved in a project. Over that time, I came across a number of barriers, setbacks, and obstacles that required working around. The very first month we were due to meet the cohort for the first time, the speaker sadly fell ill and had to cancel last minute.  The head of our CAMPUS team unexpectedly left the organisation quite early into the programme, causing no small amount of confusion. Then, of course, the world ended and we could no longer meet up each month with far less notice to adjust than you would expect a pandemic to offer. Amidst these things were peppered other, smaller crises, inevitable in any project as ambitious as CAMPUS. Finding places for speakers to stay, keeping the cohort comfortable and cared for, broken heating systems, difficult communication, managing budgets, traversing tensions, and so on and so on and so on.

Navigating through these obstacles was difficult. As someone relatively unused to the organisation, it was hard to know how best to tackle some of these issues as they arose, especially when some months they came so thick and fast there was no time to even address the first issue before numbers two, three, and four were rearing their heads and demanding instant attention. The hours were long, with workshop weeks regularly lasting up to 60 hours (once you factor in my regular PhD work, which we must, as I was doing the internship part time). Nevertheless, these experiences were invaluable to me, and retrospect goes a long way in learning about your own capabilities and strengths. I’ve honed and practised skills, some of which I haven’t drawn on in a number of years, and some of which came to me as new at the beginning of the PhD. Time keeping, multi-tasking, budget codes, accountability, event planning, and most importantly of all for this scenario; flexibility. I had to learn how to let go of things that became less important than other tasks – something my brain, with its tendency towards hyper-focus, actively loathes. I had to learn to re-prioritise on the fly and find creative ways to achieve tasks that fell through when attempted in the standard manner. I had to be willing to drop entire activities that had been organised weeks in advance because they were no longer feasible and advocate for alternative arrangements – even when those alternatives didn’t feel quite as good or as in control. Speaking of which, I had to learn to cede control at points and listen to the desires of other parties – something I have had plenty of practise with but still find incredibly difficult when organising big events. All of these things have made me, I believe, a better researcher and better prepared to tackle the unpredictable and oftentimes chaotic world of academia.

So here is the second lesson I offer:

Be flexible. If you are wired anything like me, or maybe even if you aren’t, flexibility can be something at best inconvenient and at worst slightly traumatic. My anxious brain hates when things don’t go to plan but this is a wildly valuable skill, something that can be utilised in all walks of life (even beyond your professional world) and actually, sometimes it can lead to even better experiences. Ironically, despite my natural aversion to change, I’m a huge fan of spontaneity. Once I recognise that something isn’t working, I love the feeling of diving in the deep end and really, genuinely mixing it up. And I cannot advocate enough how many wonderful things can happen when you’re willing to take a leap of faith and do something different.

This flexibility is something that comes by necessity when working in an inter-disciplinary setting. For me, I came from the world of human geography, into computer science, and dived head first into working an internship at an art gallery. As some of you will be all too painfully aware of, when you try to explain your interdisciplinary PhD to someone new, one of the first things they might ask is ‘how does all of that go together?’. At this point you get to make the decision of taking three hours trying to explain how, for example, geography, computer science, and art go together; or you can crack a joke and go home and cry because when you laughingly responded “I’ll let you know when I work it out” you weren’t kidding and some days it feels like you never actually will work it out. Is that relatable?

The point is that for some of you, just like it did for me, starting an internship in a brand new area that you may only have tangentially or informally accessed before can be thoroughly overwhelming. Those questions about how everything goes together can be enhanced fourfold when you start working in this new area and you’re introduced to all the complexities and nuances and politics that you hadn’t even gotten around to considering yet. When I first got to the Nottingham Contemporary I felt completely lost. I’d never worked in an office before, I didn’t know the arts sector, I’d never used the programmes or software they were using, and I didn’t know the acronyms. But I quickly picked up the bits I needed to (with a lot of help from a wonderful lady named Mercé) and I enjoyed the challenge. Submerging myself in one arts organisation quickly allowed me to make connections with other organisations in Nottingham. I met some really interesting people and we had some fantastic conversations about my PhD. Some of those people and organisations have even gone on to help me further with my research, offering venues and advertising and participants.

So my third piece of advice is this:

Be immersed – In some ways, this is where some of the most important value of the internship comes from – from being able to absorb yourself in this new culture and learn about the reality of it, and not just what you can gleam from choice snippets of media or literature. From networking and meeting people, to cementing your new understanding in the actual contexts that the research you’re doing will ultimately impact – there’s no better way to understand the role of your research than to become immersed in that world.

When I completed the first draft of this blog with just the three pieces of advice in it, I spent a full weekend uhming and ahhing over whether or not it was disingenuous of me to leave it on such a note. Because I also have advice to offer from the things that went wrong throughout this process – the things I wish I’d done differently. The tea, if you would, which may cast a less rose-tinted light on this internship and hopefully may work as a warning for you. Detailed below is a combination of my own failings, failings from the organisation, and failings of no-one in particular which nevertheless deeply affected the internship as an experience.

When I first started the internship, I didn’t set up enough boundaries, I didn’t talk enough about my expectations for the internship and my skills that I could offer. I was so excited about the programme and getting involved in the organisation that I didn’t stop to question what precisely my role would be. And what that resulted in was an internship in which I feel like I wasted huge chunks of those nine months printing agendas and booking hotels – valuable skills to be sure, but skills I already possessed and skills which, more importantly, don’t bear any relevance to the PhD. This failure on my part also meant that when the head of the CAMPUS team unexpectedly left, there was no-one left on the team who really knew who I was or what I could offer. Now this one is a little bit ‘six of one, half a dozen of another’, because whilst I didn’t then go to the next person and rectify my mistake, actually there were several months where it was massively unclear who was in charge. The person who moved on left behind her a gap that nobody seemed able to fill, and that left me floating in an existential puddle of uncertainty. This meant I had almost entire days where I would literally be sat twiddling my thumbs because there was no-one around to go to and ask for guidance. I made the best of these days I could, preparing several months ahead where possible and assisting other people in the office, but rarely – outside of the workshop weeks – did I have full days where I was busy. This lack of communication on my part, and lack of coherence on the organisations part, meant that overall, I found the internship to be incredibly frustrating. It also meant that when an opportunity arose to collect some data relevant to the PhD, I was denied because the person who might have backed the data collection was no longer there to support me and I had given up on searching for accountability. Although this final section is a warts and all revelation, I feel I should share this lesson so that you might learn from my experience and avoid some of the same pitfalls.

So this bonus piece of advice is:

Set boundaries – Just because you are working for a new organisation doesn’t mean that you can’t speak out and establish your needs as important. Some of what happened in my internship was unavoidable, but other parts could have been handled better if I had entered with the confidence to assert myself. Three months is a sizeable chunk of your PhD time, and you need to enter into it knowing what you’re going to get out of it.

So that’s it, the three-and-a-bonus pieces of advice I feel qualified to offer after completing my internship. Be uncomfortable, be flexible, be immersed, and set boundaries. I hope you found this interesting to read, and if you have any more questions about my internship or yours, then you can contact me at harriet.cameron@nottingham.ac.uk

Peace and love,

Harriet ‘Alfie’ Cameron


[1]          G. K. Bhambra. (2020). Gurminder K Bhambra. Available: https://gkbhambra.net/ (Accessed: 25/06/2020)

[2]          C. Conderelli. (2020). Céline Condorelli. Available: https://celinecondorelli.eu/ (Accessed: 25/06/2020)

[3]          Q. Gario. (2020). Quinsy Gario. Available: https://en.wikipedia.org/wiki/Quinsy_Gario (Accessed: 25/06/2020)

[4]          Nottingham Contemporary. (2019). Homepage. Available: https://www.nottinghamcontemporary.org/ (Accessed: 04/03/2019)

[5]          Nottingham Contemporary. (2019). CAMPUS Independent Study Programme. Available: https://nottinghamcontemporary.org/exchange/campus/ (Accessed: 07/05/2020)


Nostalgic Gifting – Call for participants

Rebecca Gibson (2019 cohort) is currently recruiting for the Nostalgic Gifting study:

What is the study for? 

We are interested in exploring nostalgia within hybrid gift-giving. Hybrid gifting in this case is a physical gift which includes a digital aspect. We are looking to recruit pairs of participants, with one participant being the gift-giver and one being the recipient.  

What would I have to do?

  • Create a gift online. 
  • Upload a photograph to accompany your gift.  
  • Take part in a 30-minute interview.  
  • Reflect upon your experience.
  • Receive a gift! 
  • Take part in a 30-minute interview.  
  • Reflect upon your experiences.


  • The study is entirely online.  
  • Gift-givers will use a website to create the gift. 
  • Photographs will be attached digitally to the gift.  
  • Gifts will be posted to recipient’s home address.


  • 18 years or over. 
  • You do not have any severe food allergies or dietary requirements.  
  • You own a smart phone and have access to the internet. 
  • You are based in the UK. 

What do I get? 

  • Gift-givers get to create a gift for loved one! 
  • Recipient will get to receive a gift.  
  • Contribution to an emerging gifting paradigm.  

Process Overview  

  • Send the person you wish to give a gift to this information/ask them to see if they would be interested. 
  • If yes, then email Rebecca making sure to copy your selected recipient in.  
  • The recipient will then be sent a screening form to check for any allergies and to ensure they are safe to receive post. 
  • If they pass the screening form and you are both happy to take part in the study then you will be sent consent forms.  
  • The recipient will then provide us with shipping details. 
  • The gift-giver will then receive a link to create the gift online. 
  • The gift will then be shipped to the recipient.  
  • The gift will arrive!
  • The gift-giver will be interviewed. 
  • The recipient will be interviewed.  


If you have any questions about the study, please contact: 









Interview Study: Legal aspects of the use of smart devices

PhD student Stanislaw Piasecki (2018 Cohort) is looking at the legal aspects of the use of smart devices by vulnerable people. Stan is currently recruiting participants to take part in an interview-based study.

post by Stanislaw Piasecki (2018 cohort)

I am a third year PhD student at the Horizon Centre for Doctoral Training (University of Nottingham). In my thesis, I am analysing how data protection law works in theory and in practice when vulnerable people use smart home devices. For this reason, I am looking to interview (for no more than 30 minutes), persons working on data protection law compliance issues in a smart home context. This could be people designing such devices, advising organisations or thinking about the challenges.

The interviews will be turned into a report, which I could send to the interviewee once I have finished my analysis. Hopefully, my findings will support data protection law compliance efforts. If requested in the consent form, all data provided during the interview will be anonymised before any publication.

My contact details are: stanislaw.piasecki@nottingham.ac.uk

I will be grateful for any suggestions.

Many thanks,
Stanislaw Piasecki

Whole Brain Emulation (WBE) and the Creation of a Substrate Independent Mind (SIM)

Angela Thornton (2019 cohort), who is partnered with Carboncopies Foundation,  had the opportunity to give a presentation at the Foundation’s Journal Club this past weekend.

Angela talked about her doctoral thesis which looks at Whole Brain Emulation (WBE) and the Creation of a Substrate Independent Mind (SIM).  After her presentation, Angela took questions on WBE.

Angela’s presentation:


Call for Participants: Mental workload in daily life

Fourth-year Horizon CDT PhD student Serena Midha is recruiting participants to take part in a research study.

Serena is researching mental workload from a daily life perspective. Serena and her team are aiming to gather a full 5 days of subjective workload levels, as well as data on what activities were being done to generate these ratings. They also want to further their understanding of people’s personal experiences of mental workload.

Participant requirements:

      • Android Users
      • Office workers outside of academia
      • Without clinical history of anxiety or depression

Participants will be offered £75 for participating in the study.

More information about the study can be found here.

You can contact Serena with any queries.


You can check out Serena’s Research Highlights here: https://highlights.cdt.horizon.ac.uk/students/psxckta



Call for Children & Young People participants

Horizon CDT PhD student Mel Wilson (2018 cohort) has recently launched an online survey to look at Children & Young People’s (CYP) contact with family, friends and social networks during the pandemic.

post by Mel Wilson

For nearly a year we have all been under greater stress due to the restrictions and changes to our lives as a result of the measures in place nationally and globally to limit the effects of Covid-19.

But what has that meant for children and young people?

I was previously researching resilience and vulnerability in school settings but these changes have meant, like many of those addressing the needs of children and young people, that access to those groups has been limited.

My studies have shifted to researching the effects of the last year on the resilience and vulnerabilities of children and young people.

Please share and ask your CYP to complete the following if they can:

Children & Young People’s contact with family, friends and social networks during the pandemic

Primary-age students survey

Secondary-age students survey

I currently have 3 studies running and am actively seeking further participants, children, young people and adults. Please see www.melaniewilson.uk for more details and links to contribute to these studies.

Thank you!