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<div class="WordSection1">
<p class="MsoNormal">***apologies if you receive this more than once***<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><u>This email is aimed at PhD students and/or early career researchers<o:p></o:p></u></p>
<h1><b><span style="font-size:14.0pt;line-height:106%;font-family:Ebrima">Call for reviews of literature<o:p></o:p></span></b></h1>
<p class="MsoNormal"><span style="font-size:12.0pt;font-family:Ebrima">The Alan Turing Institute is seeking to commission literature reviews to inform future research directions.<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-family:Ebrima">Each review should be up to 5,000 words in length and should summarise relevant academic and policy literature, identify knowledge gaps and highlight opportunities for future work. They should be written
for an interested non-specialist audience and should set out the key questions in the field, the empirical challenges to answering these questions, the approaches existing work has taken, and the results found. The reviews should be international in scope.<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-family:Ebrima">To apply, please send an email to Mark Briers (</span>london-arc@turing.ac.uk)<span style="font-family:Ebrima"> briefly describing:<o:p></o:p></span></p>
<p class="MsoListParagraphCxSpFirst" style="text-indent:-18.0pt;mso-list:l3 level1 lfo1">
<![if !supportLists]><span style="font-family:Symbol"><span style="mso-list:Ignore">·<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]><span style="font-family:Ebrima">Which review(s) you are interested in;<o:p></o:p></span></p>
<p class="MsoListParagraphCxSpMiddle" style="text-indent:-18.0pt;mso-list:l3 level1 lfo1">
<![if !supportLists]><span style="font-family:Symbol"><span style="mso-list:Ignore">·<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]><span style="font-family:Ebrima">The researchers who would be involved and their suitability for conducting the review;<o:p></o:p></span></p>
<p class="MsoListParagraphCxSpMiddle" style="text-indent:-18.0pt;mso-list:l3 level1 lfo1">
<![if !supportLists]><span style="font-family:Symbol"><span style="mso-list:Ignore">·<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]><span style="font-family:Ebrima">The focus and scope your review would take and the literatures you would draw on;<o:p></o:p></span></p>
<p class="MsoListParagraphCxSpLast" style="text-indent:-18.0pt;mso-list:l3 level1 lfo1">
<![if !supportLists]><span style="font-family:Symbol"><span style="mso-list:Ignore">·<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]><span style="font-family:Ebrima">Permission from your academic lead (where relevant).<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-family:Ebrima">The deadline for applications is Friday 25<sup>th</sup> October 2019 (4pm BST). Selection will be based on technical suitability. Payment will be negotiated based on experience and suitability. Reviews would
be expected to commence as soon as practically possible and be complete by 31 December 2019.<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-family:Ebrima">Any questions should be addressed to Mark at the above email address.
<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-family:Ebrima"><o:p> </o:p></span></p>
<p class="MsoNormal" style="mso-margin-top-alt:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:18.0pt;text-indent:-18.0pt;line-height:106%;mso-list:l2 level1 lfo2">
<![if !supportLists]><b><span lang="EN-US" style="font-family:Ebrima"><span style="mso-list:Ignore">1)<span style="font:7.0pt "Times New Roman"">
</span></span></span></b><![endif]><b><span lang="EN-US" style="font-family:Ebrima">Audio at the Edge<o:p></o:p></span></b></p>
<p class="MsoNormal"><span lang="EN-US" style="font-family:Ebrima">There is a requirement to understand the limitations of AI at the edge in terms of Size, Weight, and Power combined with model accuracy. We require an understanding of the tradeoff between model
size and performance at the edge, and to demonstrate through tangible examples the benefit of moving data enrichment to the edge of the network. There is a specific interest in the deployment of speech to text and speech recognition models on edge devices.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-family:Ebrima">Over the last couple of years there has been significant development in the application of Deep Learning pipelines to audio processing with particularly relevant work from the Google AI team
(“Streaming End to End Speech Recognition for Mobile Devices” using an RNN Transducer network), and Nvidia’s GPU acceleration of the Open source Kaldi library (</span><a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdevblogs.nvidia.com%2Fnvidia-accelerates-speech-text-transcription-3500x-kaldi%2F&data=01%7C01%7C%7Cd1480f312d104718540e08d7521c95f6%7C4a5378f929f44d3ebe89669d03ada9d8%7C0&sdata=%2B%2BYVhyIPw%2Ffd7K58ctnvcIWrQH39j4Ka%2BGK2baSKuns%3D&reserved=0" originalsrc="https://devblogs.nvidia.com/nvidia-accelerates-speech-text-transcription-3500x-kaldi/" shash="wCYoJoZwYK05hz0gImwYeF0drmbp++eXnIrxGfPu+ElcypTGJrNOuyVfee8t086/AVV/w2CYh7QrAUScvIeVPLS2G/zaLVi+WrLdpEYcoh6kVGQ9hx4k500qfX/akBKEbAGQDP6JdxLfqS5REmVshMZSdZl6cZkpG7bi3TouI00="><span lang="EN-US" style="font-family:Ebrima">https://devblogs.nvidia.com/nvidia-accelerates-speech-text-transcription-3500x-kaldi/</span></a><span lang="EN-US" style="font-family:Ebrima">).<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-family:Ebrima"><o:p> </o:p></span></p>
<p class="MsoNormal" style="mso-margin-top-alt:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:18.0pt;text-indent:-18.0pt;line-height:106%;mso-list:l2 level1 lfo2">
<![if !supportLists]><b><span lang="EN-US" style="font-family:Ebrima"><span style="mso-list:Ignore">2)<span style="font:7.0pt "Times New Roman"">
</span></span></span></b><![endif]><b><span lang="EN-US" style="font-family:Ebrima">Arabic OCR<o:p></o:p></span></b></p>
<p class="MsoNormal"><span lang="EN-US" style="font-family:Ebrima">There is a requirement to understand and demonstrate state-of-the-art methods in academia for OCR applied against both printed and handwritten Arabic script (Arabic is a priority but there is
also interest in other non-Latin based texts such as Cyrillic). We are specifically interested in the use of innovative methods for augmenting training data in order to improve the performance of classifiers. This may include the use of adversarial/generative
approaches in order to synthesis training data, building on approaches tested and demonstrated at previous ICDAR conferences.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-family:Ebrima"><o:p> </o:p></span></p>
<p class="MsoNormal" style="mso-margin-top-alt:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:18.0pt;text-indent:-18.0pt;line-height:106%;mso-list:l2 level1 lfo2">
<![if !supportLists]><b><span lang="EN-US" style="font-family:Ebrima"><span style="mso-list:Ignore">3)<span style="font:7.0pt "Times New Roman"">
</span></span></span></b><![endif]><b><span lang="EN-US" style="font-family:Ebrima">Model Security and Inversion<o:p></o:p></span></b></p>
<p class="MsoNormal"><span lang="EN-US" style="font-family:Ebrima">Increasingly the UK Government are looking to deploy machine learning based models onto internet based. Often these models are trained on sensitive data which has raised questions with regard
to UK policy on the classification of such models. Given the rise in the number of adversarial attacks being demonstrated against trained models, especially model inversion attacks, research is required in order to inform the handling of trained models using
an evidence-based approach.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-family:Ebrima">Use cases for the types of models of interest include: object detection, face recognition, speaker ID, Speech to text, sentiment analysis, document classification, and entity extraction. There
is a requirement to:<o:p></o:p></span></p>
<ul style="margin-top:0cm" type="disc">
<li class="MsoNormal" style="margin-bottom:8.0pt;line-height:106%;mso-list:l1 level1 lfo3">
<span lang="EN-US" style="font-family:Ebrima">Track state of the art research in model inversion approaches;<o:p></o:p></span></li><li class="MsoNormal" style="margin-bottom:8.0pt;line-height:106%;mso-list:l1 level1 lfo3">
<span lang="EN-US" style="font-family:Ebrima">Exploration of methods which are available and in-development to protect ourselves against such adversarial attacks.<o:p></o:p></span></li></ul>
<p class="MsoNormal" style="mso-margin-top-alt:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:18.0pt;text-indent:-18.0pt;line-height:106%;mso-list:l2 level1 lfo2">
<![if !supportLists]><b><span lang="EN-US" style="font-family:Ebrima"><span style="mso-list:Ignore">4)<span style="font:7.0pt "Times New Roman"">
</span></span></span></b><![endif]><b><span lang="EN-US" style="font-family:Ebrima">Real-world model poisoning<o:p></o:p></span></b></p>
<p class="MsoNormal"><span lang="EN-US" style="font-family:Ebrima">Researchers have demonstrated the ability to undertake targeted backdoor attacks against Face Recognition algorithms, which cause the system to behave erratically under real-world conditions.
This falls under the category of adversarial attack referred to as Model Poisoning. Model Poisoning is of particular concern to the UK Government as they are extremely difficult to detect once training has been completed, and many of the underpinning models
(e.g. ImageNet, BERT) are open source, with little knowledge of the training process and data.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-family:Ebrima"><o:p> </o:p></span></p>
<p class="MsoNormal" style="mso-margin-top-alt:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:18.0pt;text-indent:-18.0pt;line-height:106%;mso-list:l2 level1 lfo2">
<![if !supportLists]><b><span lang="EN-US" style="font-family:Ebrima"><span style="mso-list:Ignore">5)<span style="font:7.0pt "Times New Roman"">
</span></span></span></b><![endif]><b><span lang="EN-US" style="font-family:Ebrima">Approaches to Low-shot learning<o:p></o:p></span></b></p>
<p class="MsoNormal"><span lang="EN-US" style="font-family:Ebrima">The UK Government would like to embrace the potential capability enhancements which Deep Learning based systems offer across a number of different data modalities. These include but are not
limited to: image classification, object recognition, activity detection in video, text classification, entity extraction, sentiment analysis, face recognition, NLP, and audio processing. However, most commercial classifiers do not transfer well into the
Government domains due to slight differences in the data to which they are applied, or the nature of the classifiers. There is a requirement to train systems within Government on small volumes of data, however in most cases there is not enough training data
for deep learning systems to give reasonable performance. Low-shot learning may offer a solution to this challenge.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-family:Ebrima"><o:p> </o:p></span></p>
<p class="MsoNormal" style="mso-margin-top-alt:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:18.0pt;text-indent:-18.0pt;line-height:106%;mso-list:l2 level1 lfo4">
<![if !supportLists]><b><span lang="EN-US" style="font-family:Ebrima"><span style="mso-list:Ignore">7)<span style="font:7.0pt "Times New Roman"">
</span></span></span></b><![endif]><b><span lang="EN-US" style="font-family:Ebrima">Online Deception<o:p></o:p></span></b></p>
<p class="MsoNormal"><span lang="EN-US" style="font-family:Ebrima">Across government there are significant concerns regarding criminal use of generative capabilities in support of face and voice synthesis. Wider applications of such capabilities will significantly
degrade and undermine trust in online information and could lead to widespread deception of individuals online. This is a rapidly emerging field which requires agile research in order to stay on top of the threat, and to help develop potential counter-measures
and detection capabilities.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-family:Ebrima"><o:p> </o:p></span></p>
<p class="MsoNormal" style="mso-margin-top-alt:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:18.0pt;text-indent:-18.0pt;line-height:106%;mso-list:l2 level1 lfo2">
<![if !supportLists]><b><span lang="EN-US" style="font-family:Ebrima"><span style="mso-list:Ignore">8)<span style="font:7.0pt "Times New Roman"">
</span></span></span></b><![endif]><b><span lang="EN-US" style="font-family:Ebrima">Masquerading<o:p></o:p></span></b></p>
<p class="MsoNormal"><span lang="EN-US" style="font-family:Ebrima">Use of video-conferencing online is becoming ubiquitous in the modern age. With the increased maturity of deep fake capabilities, how can we assure individuals of the identity on the other end
of the video-conference? How long before GAN and similar based technologies reach a point where real-time masquerading is a mature threat? How is the technology developing? What are the risks now and in the near future? What methods are effective for detecting
such deception?<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-family:Ebrima">Prior research of relevance to this requirement include: pix2pix, vid2vid, Face-It, and Lyrebird.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-family:Ebrima"><o:p> </o:p></span></p>
<p class="MsoNormal" style="mso-margin-top-alt:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:18.0pt;text-indent:-18.0pt;line-height:106%;mso-list:l2 level1 lfo2">
<![if !supportLists]><b><span lang="EN-US" style="font-family:Ebrima"><span style="mso-list:Ignore">9)<span style="font:7.0pt "Times New Roman"">
</span></span></span></b><![endif]><b><span lang="EN-US" style="font-family:Ebrima">Automated Image/Video Forensics<o:p></o:p></span></b></p>
<p class="MsoNormal"><span lang="EN-US" style="font-family:Ebrima">There is a requirement to develop automated approaches for detection of fake and/or manipulated imagery and video. The use of imagery and video manipulation is widespread for both benign and
malicious purposes and takes many forms. These include:<o:p></o:p></span></p>
<ul style="margin-top:0cm" type="disc">
<li class="MsoNormal" style="margin-bottom:8.0pt;line-height:106%;mso-list:l1 level1 lfo3">
<span lang="EN-US" style="font-family:Ebrima">Imagery alteration using software tools such as Photoshop<o:p></o:p></span></li><li class="MsoNormal" style="margin-bottom:8.0pt;line-height:106%;mso-list:l1 level1 lfo3">
<span lang="EN-US" style="font-family:Ebrima">Image splicing<o:p></o:p></span></li><li class="MsoNormal" style="margin-bottom:8.0pt;line-height:106%;mso-list:l1 level1 lfo3">
<span lang="EN-US" style="font-family:Ebrima">Image synthesis using GANs<o:p></o:p></span></li><li class="MsoNormal" style="margin-bottom:8.0pt;line-height:106%;mso-list:l1 level1 lfo3">
<span lang="EN-US" style="font-family:Ebrima">Video splicing (e.g. Deep-fakes)<o:p></o:p></span></li></ul>
<p class="MsoNormal"><span lang="EN-US" style="font-family:Ebrima">There is a need to understand what capabilities already exist, benchmark these capabilities against a collated dataset, understand the limitations of detection capabilities, and develop new
innovations to address these shortfalls. Desired outputs include: literature reviews, demonstrators, code, and research papers.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-family:Ebrima"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-family:Ebrima">Researchers should take the following considerations as part of the research:<o:p></o:p></span></p>
<ul style="margin-top:0cm" type="disc">
<li class="MsoNormal" style="margin-bottom:8.0pt;line-height:106%;mso-list:l1 level1 lfo3">
<span lang="EN-US" style="font-family:Ebrima">There is a greater interest in generic capabilities which are able to detect more than one type of manipulation/synthesis. Use of classifiers for specific approaches (e.g. Style-GAN) whilst interesting are of less
operational utility;<o:p></o:p></span></li><li class="MsoNormal" style="margin-bottom:8.0pt;line-height:106%;mso-list:l1 level1 lfo3">
<span lang="EN-US" style="font-family:Ebrima">Researchers should assume that no prior knowledge of the approach is known (e.g. what model is being used);<o:p></o:p></span></li><li class="MsoNormal" style="margin-bottom:8.0pt;line-height:106%;mso-list:l1 level1 lfo3">
<span lang="EN-US" style="font-family:Ebrima">The use-case is to find a manipulated image or video in a large volume of un-manipulated data, therefore false positives are a concern;<o:p></o:p></span></li><li class="MsoNormal" style="margin-bottom:8.0pt;line-height:106%;mso-list:l1 level1 lfo3">
<span lang="EN-US" style="font-family:Ebrima">Modern day cameras often introduce some post processing at the sensor end (e.g. contrast stretching) which can manifest as a manipulation feature for automated detectors - we would like to minimise and account for
these type of distractors;<o:p></o:p></span></li><li class="MsoNormal" style="margin-bottom:8.0pt;line-height:106%;mso-list:l1 level1 lfo3">
<span lang="EN-US" style="font-family:Ebrima">The capabilities should be completely automated and should minimise the need for a human forensics expert.<o:p></o:p></span></li></ul>
<p class="MsoNormal"><span lang="EN-US" style="font-family:Ebrima"><o:p> </o:p></span></p>
<p class="MsoNormal" style="mso-margin-top-alt:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:18.0pt;text-indent:-18.0pt;line-height:106%;mso-list:l2 level1 lfo2">
<![if !supportLists]><b><span lang="EN-US" style="font-family:Ebrima"><span style="mso-list:Ignore">10)<span style="font:7.0pt "Times New Roman"">
</span></span></span></b><![endif]><b><span lang="EN-US" style="font-family:Ebrima">ML/Transformations on low side encrypted data<o:p></o:p></span></b></p>
<p class="MsoNormal"><span lang="EN-US" style="font-family:Ebrima">Within UK Government there is an increased push to conduct data processing and analysis on internet connected systems, to reduce storage and processing costs, and facilitate access to state-of-the-art
developments in the commercial world. However, adoption of connected systems is often hindered by the classification of datasets processed within government. Making use of developments in Privacy Enhancing Technology (PET), users are able to store and query
data on such systems in a secure manner. We need to understand whether this is possible, and what practical technologies exist which enable UK Government to conduct more data analysis on encrypted data held on internet connected systems.<o:p></o:p></span></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Best wishes<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Susan<o:p></o:p></p>
<p class="MsoNormal"><span style="mso-fareast-language:EN-GB">_____________________________________________<o:p></o:p></span></p>
<p class="MsoNormal"><b><span style="mso-fareast-language:EN-GB">Susan Davies<o:p></o:p></span></b></p>
<p class="MsoNormal"><span style="mso-fareast-language:EN-GB">Coordination Manager,
<a href="https://www.southampton.ac.uk/wsi/index.page?">Web Science Institute</a><o:p></o:p></span></p>
<p class="MsoNormal"><span style="mso-fareast-language:EN-GB">University Liaison Manager,
<a href="https://www.southampton.ac.uk/wsi/alan-turing-institute/alan-turing-institute.page">
The Alan Turing Institute</a><o:p></o:p></span></p>
<p class="MsoNormal"><span style="mso-fareast-language:EN-GB">Room 3041, Building 32<o:p></o:p></span></p>
<p class="MsoNormal"><span style="mso-fareast-language:EN-GB">Web Science Institute<o:p></o:p></span></p>
<p class="MsoNormal"><span style="mso-fareast-language:EN-GB">University of Southampton<o:p></o:p></span></p>
<p class="MsoNormal"><span style="mso-fareast-language:EN-GB">Southampton SO17 1BJ<o:p></o:p></span></p>
<p class="MsoNormal"><span style="mso-fareast-language:EN-GB"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="mso-fareast-language:EN-GB">023 8059 3523 | 07768 266464<o:p></o:p></span></p>
<p class="MsoNormal"><o:p> </o:p></p>
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