Wellbeing festivals

Wellbeing Festivals will take place at both Canterbury and Medway campuses. The events are organised by Student Support and Wellbeing who encourage students and staff to ‘enrich your life and try something new for free!’

Details of the events will be published on @unikentssw on Twitter, Facebook and Instagram.

In addition, staff and students from the School of Computing at Canterbury have the opportunity to meet Monty, a canine companion.

Medway – Tuesday 20 March 2018

The Wellbeing Festival runs from between 12.00-15.00 in the Student Hub and Drill Hall Library. Discover a variety of fun and practical ways to improve your wellbeing.

Internal departments and external organisations will take part in the day, including mental health and charity stands, Drill Hall library wellbeing, GK Union, mindful colouring, Medway Activities, Medway Park leisure centre and healthy snacks.

Canterbury – Friday 23 March 2018

Monty is a canine companion who would love to meet you. He will be on The Shed veranda from 10.00-12.00. Staff and students welcome (but no cats please!).

The main festival will be in Eliot Dining Hall between 12.00-15.00. There is an exciting line up, such as Espression Arts café, yoga sessions, a bush craft workshop, poetry therapy, mindful colouring, various mental health and charity stands, representatives from the LGBT network, the Chaplaincy, the Sports Centre and Kent Union, refreshments and live music.

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Kent wins National Capture the Flag competition

Students from the School of Computing at the University of Kent have won the annual Deloitte Cyber ‘Capture the Flag’ competition.

The competition tested students’ penetration testing, forensics, malware and intrusion analysis skills in a fun and competitive environment.

The Kent team, comprised of Matthew Boakes, Calvin Brierley, Catalin Irimie and Jamie Pont, qualified in an initial round. Only the eight highest scoring university teams were then invited to compete in the all-day final held at the Deloitte offices in Central London on 26 February.

The team won a £3000 prize. PhD student Jamie Pont said ‘I feel like I learned a huge amount and met some great people. It was very exciting towards the end; the second place team were only 100 points behind our winning score of 2500.’

Tasks included:

  • Breaking poorly configured encryption schemes that are widely used in real life
  • Finding vulnerabilities to exploit a fictional cryptocurrency exchange, enabling the exfiltration of funds
  • Using tools and techniques to extract secret information hidden within files
  • Performing forensics on memory, filesystems and network traffic
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Expert comment: Government’s AI tool will help in online terror fight

In response to the government unveiling an AI tool that can be used to block terrorist content before it uploaded to the web Professor of Computing Ian McLoughlin has said that the success of the technology will only improve over time as it continues to learn.

‘While the company behind this algorithm is extremely sensitive to revealing any details, we know that it’s based on machine learning technology. From the graphics shown during the BBC interview we can infer that the tool works on a frame-by-frame basis (and is possibly a WaveNet approach). This means it doesn’t analyse a recording in its entirety, but analyses each individual frame of the video.

‘Because actions and words in a video are very much related to the context of what has happened before (i.e. individual frames are not really important in isolation but in the few seconds of time that forms their context), there needs to be something in the algorithm that ties context of frames together, and that may be key. The biggest benefit of a frame-by-frame analysis would be to detect embedded content, i.e. segments of terrorist propaganda embedded in an otherwise innocuous video. A secondary benefit is being able to operate on real-time data (i.e. material as it is being broadcast).

‘They mentioned that there are more than 1,000 videos, and I suspect they probably used almost all of those for training. For any machine learning system, final performance is related to the inherent ability of the analysis and processing technique, plus the quality and quantity of the training material. As time goes by, performance is clearly likely to improve.

‘On the topic of performance, if 94% of videos were correctly recognised with 99.995% accuracy, the big question is what happened to the 6% that were not mentioned? Were those actual terrorist content that would be missed (false negative),or legitimate content that was incorrectly flagged (false positive)?

‘The cost of the former is that something dangerous slips through; the cost of the latter is that a human – who would need to review any flagged content anyway – is loaded with additional work. It is important to analyse the errors in any AI system, and this is no exception. However revealing the characteristics of this performance – i.e. which 60 videos are not captured –  and especially revealing what kinds of videos are correctly and incorrectly recognised, would give too many secrets to those who are producing such material.

‘In summary, a great result from ASI Data Science. Technology doesn’t stand still, and this will need to be improved as terrorists evolve their approaches, but let’s try to keep the exact technology and performance secret in the meantime.’


The University’s Press Office provides the media with expert comments in response to topical news events. Colleagues who would like to learn more about how to contribute their expertise or how the service works should contact the Press Office on 3985 or pressoffice@kent.ac.uk

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Placement Officer wins Kent photography competition

Katie Van Sanden, Placement Officer in the School of Computing, has been announced as a winner in the Vice-Chancellor’s photographic competition. Staff, students and alumni were invited to submit photographs that captured the essence of the beautiful cities in the UK and Europe where the University of Kent has campuses and centres.

Katie’s photograph ‘Artist at work amongst the pillars of Rome’ was one of 130 entries and was announced as the winner for Rome. Katie’s black and white photo will now be displayed in the Vice-Chancellor’s office, alongside the other winners.

The judges commented on the creativity of the winners and their interpretations of the centres, cities and what it means to be the University of Kent in these places.

The other winners were;

Athens – Marina Watt, Staff

Paris – Alice Helliwell, Student

Brussels – Vanessa Wyns, Student

Medway – Emma Harrington, Staff

Canterbury – Simon Hicks, Staff and Anamika Misra, Student

Honourable mention for Canterbury – Claire Dowling, Student

All of the entries are available on Flickr

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Information retrieval and web search expert joins School of Computing

The School of Computing has added to its talented staff with the appointment of Shoaib Jameel, a Lecturer based at the Medway Campus. As well as teaching on the Computing programmes, Shoaib will be an active member of the Data Science Group, continuing his research in probabilistic topic models, Bayesian nonparametric statistics, vector space embeddings, natural language processing, optimization, and information retrieval.

Shoaib Jameel gained his Bachelor’s degree in Computer Science and Engineering in India, then completed his PhD programme in the Department of Systems Engineering and Engineering Management at the Chinese University of Hong Kong in 2014.

After his PhD, he worked at Cardiff University as a postdoctoral researcher  on vector space embeddings for flexible reasoning with structured knowledge. Shoaib has collaborated with researchers from both industry and academia, for example, Microsoft Research, Carnegie Mellon University, NTT Communications (Japan), Noah’s Ark Labs (Hong Kong) and the Institute of Infocomm (Singapore). Shoaib has consistently published in several highly selective conferences and journals, such as SIGIR, TOIS, CIKM, ECAI and AAAI. He has also served as a programme committee member for several top-tier conferences and has reviewed for leading journals. His works have been funded by the Hong Kong Government and Microsoft Research Asia.

He said; “I am really excited to start a new role in the University of Kent. The staff at Medway are so friendly and I enjoy working with them. I want to make notable contributions in both teaching and research at Kent. I am passionate about information retrieval and web search. Web search is always challenging due to the huge amounts of data that is available online. Indexing and searching for relevant information on such massive collections of data is a serious task. The aim of my research is to propose novel scalable computational models for retrieval and relevance ranking. My vision is to see web search engines robust enough to handle spamming and bias against a particular person or community. I also aim to incorporate social network data into retrieval ranking problems.”

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New lecturer joins School of Computing

Dr Özgür Kafalı has joined the University of Kent as a lecturer in the School of Computing. Özgür is a member of the Cyber Security Research Group and is based at the Canterbury campus.

Özgür Kafalı received his undergraduate, MS, and PhD degrees from Boğaziçi University, one of the top universities in Turkey. During his MS and PhD, he developed Artificial Intelligence agents for e-commerce as well as working as a software developer and architect in the industry. After finishing his PhD, he got his first postdoc appointment at Royal Holloway University of London, where he worked on an EU project aiming to improve the lives of diabetic patients. He then moved to North Carolina State University to experience research practices on both sides of the ocean. His work was funded by the Science of Security project from the National Security Agency of the US.

“I always had an interest in cybersecurity and privacy research, especially regarding human factors and sociotechnical systems. I am delighted to be able to pursue my research interests and disseminate them further to students as a lecturer at the University of Kent.”

Özgür has constantly published his research in prestigious venues for Artificial Intelligence and Software Engineering. Özgür’s views on sociotechnical cybersecurity and privacy are recognized in a recent IEEE online article.

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Year in Computing – application deadline 16 February

The ‘Year in Computing’ is a self-contained year of study offered by the School of Computing to students from any other School in the University*.

Applications for September 2018 are now open and need to be received by 16 February.

 Apply now

Students who successfully complete the year graduate with their original degree title plus ‘with a Year in Computing’.

The Year in Computing will especially be of interest to you if;

  • you are interested in studying computing AND their current degree,
  • you would like to get prepared for a career in tech,
  • you are interested in exploring the frontiers of their subject and computing,
  • you want to learn how to be creative with computing.

The innovative Year in Computing programme has been awarded a University teaching prize. Programme director Ian Utting said: ‘The programme exemplifies the University’s aims to allow students to broaden their programmes in a flexible way.  It promotes links between different areas of study and allows students with broad interests and ambitions to realise them. It also provides work-related skills to support students in their future study, research or careers and this aligns strongly with the government’s UK Digital Strategy’.

*with the exception of students from the School of Computing and School of Psychology.


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Alumna wins Tech Women 50 award

Computing with a Year in Industry Alumna, Charlotte Hutchinson, has been named one of the top 50 women in technology in We Are Tech Women‘s  TechWomen50 awards 2017, which celebrates emerging talent.

Over 500 women were nominated for the awards which were judged by a panel of 20 independent judges. Over 15,000 public votes were then received for the 100 shortlisted nominees.

Charlotte graduated from Kent in 2015 and went to work at IBM following completion of a placement year at IBM in 2013. As a placement student Charlotte worked in a level 2 technical support role solving complex problems for customers in Europe, Middle East and Africa.

On re-joining IBM as a graduate Charlotte became the European Lead SME for an API (application programming interface) platform. Charlotte operates as a Technical Consultant for IBM Cloud, specialising in the API economy. She has presented to numerous groups of technical experts, run workshops and provided specialist knowledge to clients regarding architectural principals related to an API strategy. She presented at IBM’s biggest conference in Las Vegas.

Charlotte has had six articles published on IBM Developer Works, around APIs and assisted in creating two IBM certification exams. She also has three filed patents and has four patents pending search.

Congratulations to Charlotte on her award and we wish her every success in the future.

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DeepMind: can we ever trust a machine to diagnose cancer?

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Srivas Chennu, University of Kent

DeepMind has recently announced a fresh collaborative partnership with the UK’s health service, with plans for the artificial intelligence firm to develop machine learning technology to research breast cancer.

DeepMind, a Google subsidiary, is perhaps best known for successfully building AI that is now better than humans at the ancient game of Go. But in recent months – when attempting to apply this tech to serious healthcare issues – it has been on the sidelines of a data breach storm.

In July, DeepMind’s collaboration with London’s Royal Free hospital led to the NHS trust violating the UK’s data protection laws.

The Information Commissioner’s Office (ICO) found that Royal Free’s decision to share 1.6m personally identifiable patient records with DeepMind for the development of Streams – an automated kidney injury detection software – was “legally inappropriate”. DeepMind wasn’t directly criticised by the ICO.

Personal records included patients’ HIV-positive status, as well as details of drug overdoses and abortions. Royal Free’s breach generated considerable media attention at the time, and it means that DeepMind’s latest partnership with an NHS trust will be scrutinised carefully.

It will be working with Cancer Research UK, Imperial College London and the Royal Surrey NHS trust to apply machine learning to mammography screening for breast cancer. This is a laudable aim, and one to be taken very seriously, given DeepMind’s track record. London-based DeepMind emerged from academic research, assisted by Google’s deep pockets. It is now owned by Google’s parent company Alphabet.

Its success has arisen from recruiting some of the best machine learning and AI scientists, organising them into goal-driven teams, and freeing them up from having to teach or apply for funding.

Mind reader

DeepMind appears to have learned from the Royal Free data breach, having “reflected” on its own actions when it was signed on to work with the trust. It said that the breast cancer dataset it will receive from Royal Surrey is “de-identified”, which should mean that patients’ personal identities won’t be shared.

Another key difference is that the Royal Surrey dataset was explicitly collected for research – indicating that participants gave consent for their data to be shared in this way. DeepMind has also been upfront about its approach to data access, management and security. It has appointed independent reviewers and verifiable data audits, in the hope of building trust and confidence.

Given DeepMind’s continued collaboration with the NHS on a range of research, citizens are rightly concerned about how private corporations might exploit the data they have willingly shared for publicly funded work.

Few details about the Royal Surrey research project – which is in the early stages of development – have been released, but it’s likely that DeepMind will focus on applying deep neural networks for scanning mammogram images to automatically identify signatures of cancerous tissue. This approach would be similar to its Moorfields Eye Hospital project, where DeepMind is building automated machine learning models that can predict macular degeneration and blindness from retinal scans.

DeepMind is probably also exploring the possibility of incorporating novel deep reinforcement learning algorithms to train the machine learning models. The algorithms would then tap into insights from empirical neuroscience research about how the human brain learns from reward and punishment.

Reinforcement learning – which differs from more conventional supervised and unsupervised learning methods used in machine learning – is the technique that enabled DeepMind to train agents that learn to play Go and many other games better than humans.

Apart from these algorithmic advances, DeepMind might find, as it already has with the Streams trial, that there are many technological tweaks that can improve how doctors treat patients, without any need for machine learning at all.

Human nature

From my own experience in applying data analytics to medical diagnostics in neurology, I know that – even if things go well for DeepMind and it manages to build a machine learning model that is excellent at detecting the early signs of breast cancer – it might well face a more practical problem in its application to the real world: interpretability.

‘Inflammatory’ breast cancer, in which tumour cells spread through dermal lymphatic channels (vascular invasion) of the skin.

The practice of medicine today relies on trust between two humans: a patient and a doctor. The doctor judges the best course of treatment for a patient based on their individual clinical history, weighing up the relative pros and cons of the different options available. The patient implicitly trusts the doctor’s expertise.

But will patients or doctors trust a machine if it produced the same recommendation, based on an algorithm? While the use of machine learning has become commonplace in many contexts behind the scenes, its application in healthcare is fraught with the challenge of trust.

If a doctor or patient fails to understand and communicate the rationale behind a recommendation, it might be very difficult to convince either to adopt it. And no machine learning algorithm is likely to be perfect. Both false positives and negatives are of great consequence in the healthcare context.

In the world of policing, AI has muddily kicked up hidden biases that can creep into machine learning models, reflecting the unfairness embedded in our world. Machines that inherit our prejudices might make for unpopular medical aids.

AI scientists are working on the problems of bias and interpretability, while also working with clinicians to design artificial intelligence that is more transparent about uncertainty.

The ConversationBeyond the technological advances in AI for improving human health, both ethics and interpretation will play central roles in its acceptance.

Srivas Chennu, Lecturer in eHealth, University of Kent

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

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Prizes awarded to outstanding Master’s students

Two Master’s students from the School of Computing have been awarded prizes for outstanding achievements on their degrees.

Philip Adey, MSc Advanced Computer Science, won the prize for Outstanding Performance on the Programme with an overall mark of 87.7%.

Lewis Mckeown, MSc Computer Science with an Industrial Placement, won the prize for Outstanding Performance in the Project, with a score of 95%.

Lewis’ project entitled ‘Software for a Narrative Generation’ was supervised by Anna Jordanous. In the project Lewis investigated how we can use computational creativity (making software that can do creative things like write stories) to investigate how constraints affect creativity – i.e. In what way does it affect creativity if we are required to follow and fit in with more limitations, rules and specifications – or if we are given fewer such constraints? Lewis explored and compared four different types of story-telling software that worked with varying levels of freedom or constraints, to generate deliberately surreal stories. The work engaged well with theories about creativity in humans as well as computational creativity.

Both students received a certificate and £100. Congratulations to both of them.

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