12 July 2022,

by Chris Xiaoxuan Lu, University of Edinburgh

Hi, I am Chris, a lecturer in the School of Informatics, University of Edinburgh. My research broadly studies Cyber Physical Systems and Mobile Robotics, with a focus on unleashing their promise in challenging but critical environments, such as search and rescue, autonomous driving in bad weather and surgery robots inner body.

Funded by the SICSA Saltire Emerging Research Scheme, I managed to visit multiple world-leading research groups aligning with my research interest. These include the Prof. Koen Langendoen and Dr. Guohao Lan at TU Delft, Netherland; Dr. Claudio Paliotta from SINTEF, Norway; Prof. Kostas Alexis at NTNU, Norway; Dr. Petros Daras from CERTH, Greece; and Prof. Federico Alvarez from UPM, Spain.

The outcomes of this research exchange are fruitful and yield both new grant proposals and paper submissions. I have joined the consortium led by the CERTH researchers for the upcoming Horizon Europe bid on “Enhanced situational awareness and preparedness of first responders and improved capacities to minimise time-to-react in urban areas in the case of CBRN-E-related events”. Additionally, with the researchers in SINTEF, we have identified another Horizon Europe call in early 2023 and are now at the stage to assemble the consortium together. Due to Brexit and the uncertainty of the UK’s association result with Horizon Europe, EU researchers today are very hesitant about whether to have a UK institution in their consortium; none of the above proposals could happen this time if my Saltire exchange trip is not in place. On the side of research collaboration, Guohao Lan’s students and mine are collaborating on a project together about how to accurately track the eye gaze of firefighters in the wild and use the tracked gaze information to estimate the cognitive load of firefighters to prevent fatigue accidents during the search and rescue mission.

Last but not least, over the 3-month time, I gave six seminar talks in total at the TU Delft (two departments separately), CERTH and SINTEF (two departments separately) and UPM, Madrid. These talks improve the research visibility of my home institution (U of Edinburgh) and the theme development of the SICSA community, to some level, repairing the links with the leading researchers in the EU after the damaging impact of Brexit and COVID’19.

I would like to thank SICSA and the SFC Saltire Emerging Researcher Scheme. It was beyond just an honour to be selected but also leads to timely and practical helps to my career under the impact of Brexit and pandemic lockdown.

2 July 2022,

by Lucy Havens, University of Edinburgh

At the end of June, I finally was able to attend a conference in person as a Ph.D. student: LREC! I’m grateful to SICSA, EFI, and the Informatics Graduate School for making it possible for me to attend LREC in person this year, because it only occurs every two years. This year’s LREC, which stands for Language Resources and Evaluation, took place in Marseille, at le Palais du Pharo, a short walk from le Vieux Port (the Old Port) with lovely views of the Mediterranean Sea. In this post I share highlights from my experience.

Day 0

 

In addition to attending the conference, I presented at one of the conference’s workshops, Perspectivist Approaches to Natural Language Processing (NLPerspectives Workshop), which took place the day before the conference began.  The term “perspectivist” refers to the Perspectivist Data Manifesto, which questions the assumption that aggregated, annotated datasets can provide a “ground truth” for language models to learn from.

For anyone less familiar with machine learning: typically annotated datasets are created by having people label a collection of documents according to a set of instructions.  After many people finish labelling the documents, researchers calculate the agreement and disagreement between their labels, hoping for low levels of disagreement, and then merge everyone’s labelled documents to create one final version of the dataset.  Researchers then use the dataset to teach a language model to automatically identify what the people labelled in the documents (this approach to machine learning is called “supervised” learning).

The thing is, when everyone’s labelled data gets merged, the different perspectives that different people brought to the labelling process is erased.  When labelling complex topics (such as gender bias, my own area of research), there may be more than one label that makes sense, depending on the perspective of the person reading a document.  Researchers interested in perspectivist approaches are proposing ways to incorporate multiple people’s perspectives in a labelled dataset, and advocate for publishing non-aggregated versions of labelled data so that people’s disagreeing labels can be analyzed.

In the paper I presented at the NLPerspectives Workshop, co-authored with my Ph.D. supervisors Ben Bach, Melissa Terras, and Bea Alex, we propose the use of text visualizations for exploring non-aggregated, annotated datasets.  In order to train a language model, LOTS of data is needed, so it’s difficult to carefully read through every labeled document in a dataset to study patterns in people’s disagreeing and agreeing labels.  Since data visualization relies on intuitive visual cues to facilitate data analysis at large scale, it’s well-suited to analyzing multiple versions of a collection of labeled documents!

During the workshop, Su Lin Blodgett gave a great talk reflecting on how perspectivism complements participatory approaches to research, with perspectivism focusing on the technical decisions rather than previous participatory approaches that focus more on the problem framing and evaluation!  You can check out the full proceedings of the workshop here.

Day 1

Julia Parish-Morris gave a keynote talk titled, “Language Resources for Charting Linguistic Diversity in Neuroexpansive Populations.”  When I’d thought about diversity in the context of language resources in the past, my mind had gone to different languages and dialects (since I’m living in Scotland, some examples from there would be Lallans, Doric, Gaelic, and English).  Parish-Morris’ talk broadened my thinking about language diversity, especially about how to represent diversity in recorded language versus written language.  They pointed out how spoken language has a richness that written language does not, thanks to pauses, intonation, and the emotion behind spoken words.  Parish-Morris emphasized that the goal in creating language resources for neuroexpansive populations isn’t to improve classification, it’s to improve our communication, something that we all could work on!

During the Q&A, someone in the audience asked Parish-Morris about the use of the word “neuroexpansive,” saying they were unable to find anything about the word when they googled it.  Parish-Morris said she had adopted the word after a conversation with their brother, who is autistic and prefers “neuroexpansive” to “neurodiverse.”  The ending “-expansive” focuses on broadening our conceptualization of the world’s population, whereas “-diverse” tends to focus on trying to include more people into predefined concepts.

It’s amazing how powerful one word can be to shift a person’s thinking!  Moving forward, I’ll be trying to think more about expansion, rather than trying to fit people in to categories I’ve already defined…

Day 2

During the second day of the conference, I attended presentations in the morning and in the afternoon that caught my attention, due to my own research interests right now.  In the morning, I heard David Kletz present, “A Methodology for Building a Diachronic Dataset of Semantic Shifts and its Application to QC-FR-Diac-V1.0, a Free Reference for French.”  I’m interested in the changing meanings of words over time (a.k.a. diachronic semantic change) in the context of gender bias, so I’m looking forward to reading Kletz’s paper.

In the afternoon, I heard two presentations about different aspects of work on the same project, “Automatic Normalisation of Early Modern French” and “From FREEM to D’AlemBERT: a Large Corpus and a Language Model for Early Modern French.”  The

“normalisation” in this work referred to what’s almost a translation task, converting Early Modern French (French from the 17^th century, when word spellings were not yet fixed) to present-day French.  I’m particularly interested in reading more about the language model development process, because the researchers tried numerous experiments in an attempt to determine the best model set-up for their dataset of historical language (I anticipate I’ll need to do quite a bit of experimenting myself as I begin creating a model on my annotated dataset for my Ph.D.).

Day 3

On the last day of the conference, Steven Bird gave the Antonio Zampolli Prize Talk (he’d been awarded the prize earlier in the week).  There was a common theme in his talk and Blodgett’s at the NLPerspectives Workshop: participatory methods.  He recommended a book I’ll have to check out at the library, Orality and Literacy, and an article I’ll have to read called “Guiding Principles for Participatory Design-inspired Natural Language Processing.”  Most striking about the Prize talk, though, was that Bird told a series of stories about his mistakes.  The humility this demonstrated to me was inspiring and humbling in and of itself – Steven Bird is one of the developers of NLTK, a programming library I use ALL the time for analyzing text!  He must have had plenty of successes to talk about.  Instead, though, the message he left the audience with was what he learned from the mistakes he’d recounted: linguistic diversity is an opportunity to sit down together and talk, to develop trust across communities of people.

In poster session that afternoon, this message came back to me in a conversation with Robert Pugh.  I approached Pugh’s poster, titled “Universal Dependencies for Western Sierra Pueblo Nahautl,” while Pugh was in conversation with another person who was saying something about how they would have written a script to complete more of the work in Pugh’s project.  The project had created a morpho-syntactically annotated corpus according the Universal Dependencies framework, which serves as a standard schema for labeling the morphology and syntax of languages, which in turn makes it easier to represent different languages computationally.  Pugh said there was so much variation in the project’s annotation work, that writing the script probably would have taken longer than the work had manually.  He went on to say (more importantly in my opinion!) that it was also valuable to spend time with the data up close, reading through the language attentively as a researcher trying to develop computational resources for the language.

Speaking as someone whose work often crosses disciplinary boundaries into the humanities, I appreciated Pugh stating the value of spending time with the language data.  Sometimes it starts to feel like, though the value of this sort of work is taken as a given in the humanities, it needs to be extensively explained, and even fought for, in the computational sciences.

If you ask me, spending time with language in the form of people speaking it and in the form of data is something the language technology community should encourage more of!

 

8 July 2022,

by Rui Ying, University of Edinburgh

Hi, I am Rui Ying! I am a third-year PhD student at the University of Edinburgh Business School. My research interest is in financial modelling for Open Banking, where I drill down into individuals’ cash flow transactions to assess personal financial risks.

Under the SICSA’s Saltire Emerging Research Scheme, I completed a three-month research visit at the research centre for Information Systems Engineering (LIRIS) in KU Leuven. I worked closely with Prof Johannes de Smedt, who is an expert in data science and business informatics. Our project is focused on identifying risky accounts which might portray anomalous behaviours. Being able to flag risky accounts eases fraud checks where investigators can directly spot the high-risk group instead of
manually checking the accounts.

During the visit, we developed a cluster-based outlier detection model to assess anomaly risks. Specifically, we identify the profiles of financial behaviours in the first stage, and then compute the outlier scores for each account in the second stage. The anomaly risk assessment is based on the computed outlier scores where a higher score indicates a higher degree of outlyingness, and thus, a larger deviation from the conventional income and spending patterns. Throughout my research stay, I have joined several academic seminars on outlier detection which showcased innovative algorithms to capture anomalies, which are interesting new knowledge for me. I also presented my research output in LIRIS. It was an engaging presentation, with inspiring ideas and feedback that are helpful to further improve the current output.

This research collaboration has brought productive research outcomes and exciting network-building with colleagues in LIRIS. The next plan is to continue our collaboration in an online manner for further refinements of the current research output and to get the work published.

Great thanks to the Saltire Emerging Research Scheme to make this fruitful research visit possible!

14 June 2022,

by Florian Mathis, University of Glasgow

“Hoi!”, I’m Florian, a joint PhD candidate at the University of Glasgow and the University of Edinburgh. My research interests are at the intersection of Human-computer Interaction (HCI), Usable Security, and Virtual Reality (VR). Specifically, I propose, evaluate, and provide first evidence of the suitability of remote VR studies for evaluating real-world prototype systems. Sounds complex? Not at all (well, maybe a bit…)! Feel free to drop me an email (florian.mathis@glasgow.ac.uk) if you want to learn more about my research, but for now, let’s focus on something more tangible!

After spending more than half of my PhD in lockdown (and on Zoom), I decided to apply for SICSA’s Saltire Emerging Researcher Grant to visit one of my favourite usable security and privacy labs in Germany: Prof. Florian Alt’s Usable Security and Privacy group at the research institution CODE at the Bundeswehr University Munich.

During the research visit, I collaborated with undergraduate students, PhD students, postdoctoral researchers, and world-leading usable security and privacy experts. During the visit, I worked on a scoping review in the authentication field and kick-started long-term collaborations between the Bundeswehr University Munich, the Ludwig-Maximilians-University Munich, and the University of Glasgow about a) bystander awareness during productivity tasks in VR and b) shoulder surfing in the wild. Both projects will continue after the research visit and hopefully transition into practice!

I also had the opportunity to attend the CHI 2022 conference together with the Bundeswehr University Munich to meet old (and new) friends from the HCI community. There, I met several researchers and practitioners working in the broader HCI field. A few days later, I visited the newly created HCI lab in St.Gallen, where I presented and discussed my research with Prof. Johannes Schöning’s group. A highlight of the trip to St.Gallen was Prof. Johannes Schöning’s inaugural lecture, which was a fantastic mix of scientific research and Homer Simpson (https://twitter.com/JohannesSchoeni/status/1526850973632708608).

Overall, the research visit was a fantastic opportunity to learn more about neighbouring

fields, learn from other PhD students’ works, and showcase the research we do at the University of Glasgow and at the University of Edinburgh. While my time in Prof. Florian Alt’s research group is over, there are lots of ongoing collaborations and many more projects planned! Our collaborations will continue to grow and hopefully result in some real-world impact.

Finally, I want to thank SICSA and the University of Glasgow’s Mobility Funding for supporting this research visit! I hope that my experiences inspire and motivate fellow PhD students to consider a research visit as part of their PhD: There is more to PhD than writing papers.

31 May 2022,

by Sarah Thomson, University of Stirling

I’m Sarah, and I’m a research fellow at the University of Stirling. In 2 days, I will become a lecturer there.

At Stirling, I’m part of the Data Science and Intelligent Systems Research Group and my specialty is evolutionary computation (EC) – algorithmic approaches to optimisation which draw inspiration from natural evolution. As a postdoc, my main project has been using machine learning and evolutionary computation to build a system which personalises and optimises schedules for vocational rehabilitation. My PhD was on fitness landscapes, which are a mathematical tool to study how optimisation problems and optimisation algorithms interact.

I recently returned from a month-long research visit to Michigan State University (MSU) in the United States, which was facilitated by SICSA PECE funding. At MSU I visited, collaborated, and networked with Professor Wolfgang Banzhaf — a world leader in evolutionary computation — and Dr Kenneth Reid, a promising early-career researcher with a background in both EC and genetics.

During the research visit, I developed a prototype system which evolves different “variants” of exam-question code snippets using a field of EC called “genetic improvement” (GI). The GI can replace binary or unary operators, or delete or swap code lines. The underlying motivation for this is that hopefully in the future, a more developed version of this tool can produce a different variant of an exam-question code snippet for each student, to reduce the chances of cheating. Every variant evolved is different. At the moment, the prototype works for two exam-question programs. In the next stages of the project, we intend to add more questions, as well as extending ways that the code can be evolved. Additionally, we plan to publish an article on this work once it is sufficiently well-developed.

While at MSU, I presented a seminar to the Michigan portion of the globally-known BEACON (Center for the Study of Evolution in Action) consortium. This was well received, with many intriguing and inspired questions. In addition to this, I also networked at social events with MSU scholars, as well as a visiting scholar from Portugal.

Massive thanks to SICSA for facilitating this productive and highly enjoyable visit!

27 May 2022,

by Theo Stouraitis, University of Edinburgh

My name is Theo, I am a research associate at the Statistical Machine Learning and Motor Control (SLMC) group at the University of Edinburgh (UoE). At the beginning of April, I returned back to Scotland, UK after a three-month research visit at the Interactive Robotics Group (IRG), at the Massachusetts Institute of Technology (MIT), Cambridge, MA, USA.

There, I worked together with Prof. Julie A. Shah who leads the IRG and Shen Li, a PhD student at IRG, on a joint project. Our research is focused on modelling the uncertainty of humans’ behaviours when performing a task in collaboration with another agent, such as a robot. Modelling uncertain human behaviour allows robots to decide when to move conservatively and when efficiently such that the human partner is always safe. To capture the uncertainty, we utilize mathematical objects that are called sets and we developed uncertainty-aware algorithms for estimation and prediction of the human behaviour that enable safe robot motion generation.

As an example, let’s consider a robot assisting a human to get dressed. As the cloth might occlude parts of the human body, like the human elbow, the robot needs to rely on alternate sensory inputs, such as sensed forces, towards estimating the posture of the human arm. Such estimates are likely to be erroneous to an extent, thus a robot should be able to evaluate the confidence that it has in these potentially inaccurate estimates of the human state to act accordingly and with caution.

In terms of outcomes, we are currently working on finalising a method that allows us to simultaneously estimate the state of the human and plan a safe path in continuous domains with constraints. Most of the work done at MIT was focused on composing the theoretical foundations of the method. Currently, we are continuing our collaboration remotely to produce experimental results on an assistive dressing task using our newly developed method and we aim to submit this work to a conference or a journal in the coming months.

In addition to this outcome, a collaborative paper that resulted from our remote collaboration prior to my visit at MIT has just recently been published. This is: S. Li, T. Stouraitis, M. Gienger, S. Vijayakumar and J. A. Shah, “Set-Based State Estimation With Probabilistic Consistency Guarantee Under Epistemic Uncertainty,” in IEEE Robotics and Automation Letters, vol. 7, no. 3, pp. 5958-5965, July 2022, doi: \doi{10.1109/LRA.2022.3154802} and its primary focus is on estimation with guarantees. This work was also featured at MIT news.

During my three-month visit at MIT, I had the opportunity to work and interact in person with all the members of the Interactive Robotics Group (IRG), as well as to participate in a number of events, where I met a wide range of academics from MIT and from other US and Canada institutions. I also had the opportunity to present the research work that we (my colleagues, my advisors and I) did in Scotland (at UoE) during my PhD at few other labs at MIT, USA and at the University of Toronto, Canada. All these interactions allowed me to grow my mindset and my network. The latter will hopefully lead to formation of new joint projects and joint events in the future. Hence, I found that my visit has been extremely stimulating and productive and I would definitely recommend it to any researcher. I believe that visiting another lab in another continent offers many new experiences and interactions that can serve as idea incubators.

Last but not least, I also had a lot of fun engaging in various MIT social activities and met a lot great people from all over the world.

Finally, I would like to thank SICSA for funding this research visit via the SICSA Postdoctoral and Early Career Researcher Exchanges (PECE) award, which greatly supported me during this fruitful research visit!

10 May 2022

by Matthew Barr, SICSA Director of Education

Congratulations to our newly-selected SICSA Education Learning & Teaching Scholars!

We received a lot of applications, and, inevitably, we couldn’t accommodate everyone. However, we hope to be able to run the programme again next year, if funding allows.

Our 16 Scholars will now embark on a programme of meet-ups, mentoring, and networking, designed to develop new scholarship ideas and explore innovative teaching practice.

This year’s successful Scholars are as follows:

Areti Manataki	University of St Andrews
Alistair McConnell	Heriot-Watt University
Aurora Constantin	University of Edinburgh
Charlotte Desvages	University of Edinburgh
Chris McCaig	University of Glasgow
Cristina Adriana Alexandru	University of Edinburgh
Isla Jean Elizabeth Ross	University of Strathclyde
Jamie O’Hare	Abertay University
Konstantinos Liaskos	University of Strathclyde
Martin Goodfellow	University of Strathclyde
Mireilla Bikanga Ada	University of Glasgow
Mun See Chang	University of St Andrews
Nur Syibrah Binti Muhamad Naim	University of Strathclyde
Oana Andrei	University of Glasgow
Peter Chapman	Edinburgh Napier University
Tiffany Young	Robert Gordon University

 

6 May 2022

by Dr Mireilla Bikanga Ada, SICSA Education Champion and SICSA Education Distinguished Speaker Seminar organiser at UofG.

The School of Computing Science at the University of Glasgow held a SICSA Education Distinguished Speaker Seminar on 30th March 2022. The event was organised by the University’s SICSA Education Champion, Dr Mireilla Bikanga Ada.

The Centre for Computing Science Education (CCSE) team in the School of Computing Science, led by Professor Quintin Cutts, were very pleased to receive an international expert, Dr Lauren Margulieux, a professor of Learning Sciences at Georgia State University. Dr Margulieux received her PhD from Georgia Tech in Engineering Psychology, the study of how humans interact with technology. Her research interests are in educational technology and online learning, particularly for computing education. She also coordinates an initiative in Georgia State’s teacher preparation programs to integrate computing into pre-service teacher training in all disciplines and directs a computer science endorsement to certify in-service teachers to offer computing courses. She focuses on spreading computational literacy and the use of computing to achieve personal and professional goals.

Dr Margulieux gave two great talks on the following topics:

Title: Building Theory in STEM Education Research: Multiple Conceptions Theory
Abstract: The computing education research field frequently calls for theory-building work to better explain the mechanisms of how people learn computer science. This talk discusses a theory that has been developed based on a synthesis of work across multiple fields to explain phenomena frequently seen in computing education. Multiple Conceptions theory proposes a mechanism to explain how both direct instruction and constructivist instructional approaches can be designed to guarantee successful results. It draws upon instructional approaches from various STEM fields and educational psychology.

Title: Computing Education Research Methods and Design
Abstract: Computing education research draws from methodology in the social sciences, like education, psychology, and learning sciences, to conduct research with learners. Learners aren’t like molecules in a beaker or mice in a cage; they bring a lot of variability to the research environment, both from learner to learner and within learners from context to context. Social sciences have developed methods to deal with this variability, which we will discuss in this talk. We will also discuss other features of research design related to reliability, validity, and generalizability of results. The talk will focus on methods and designs particularly relevant in computing education.

The hybrid event generated interest from and was attended by members (n = 36) of various Scottish universities and beyond, including The University of Glasgow, University of Strathclyde, University of St Andrews, Edinburgh Napier University, The University of Edinburgh, Robert Gordon University, University of California San Diego, Georgia State University.

Delegates learned about variation and the application of theory in our practice. It was also an opportunity to reconnect with others and increase the opportunities for collaborative work within the SICSA community in Scottish universities and beyond, focusing on Computing Science Education Research. We are looking forward to holding a similar event again.

 

 

23 March 2022

by Theodoros Stouraitis, University of Edinburgh

I’m Theo, a research associate at the Statistical Machine Learning and Motor Control (SLMC) group at the University of Edinburgh and I am about to complete a three-month research visit at Interactive Robotics Group (IRG), at the Massachusetts Institute of Technology (MIT), Cambridge, MA, USA. During my research visit, Prof. Julie A. Shah who leads the IRG, Shen Li, a PhD student at IRG, and I jointly worked on a project that we started remotely during the pandemic.

Our joint project is focused on modelling the uncertainty of humans’ behaviours when interacting with robots. Equipping robots with the ability to estimate the uncertain state of the human and predict the uncertain motion of the human is key to guaranteeing human safety during interaction. We work on methods that describe the uncertainty with sets and we develop uncertainty-aware algorithms for estimation and prediction of the human behaviour as well as robot motion generation.

As an example, let’s consider a robot helping a human getting dressed. During dressing the robot cannot directly see the position of the human’s elbow due to the cloth, neither can predict where the human arm will be next. This in turn complicates the robot motion generation as the robot might be overconfident in an erroneous human state that might render the robot’s behaviour unsafe. Modelling uncertain human behaviour allows robots to decide when to move conservatively and when efficiently such that the human partner is always safe.

My three-month visit started in January 2022, and it will be completed at the end of March 2022. In late December 2021 and during the Omicron wave I packed my suitcase and left cold Edinburgh for the even colder Boston. The first couple of weeks were about adjustment to the new town and weather, hunting for a room and getting familiar with MIT and the IRG, while the following weeks were full of focused work, meeting students, academics and fellow roboticists at MIT, as well as exploring the particularly cold (for a Greek) and vibrant Boston. My visit has been extremely stimulating and productive and I would definitely recommend it to any researcher, especially if the visit takes place in another continent. After the completion of my visit, our collaboration between the IRG, the SLMC group, and the Honda Research Institute Europe will continue towards developing further human uncertainty models.

Finally, I would like to thank SICSA for funding this research visit via the SICSA Postdoctoral and Early Career Researcher Exchanges (PECE) award, which greatly supported me during this fruitful research visit!