UH Biocomputation Group - Artificial intelligencehttp://biocomputation.herts.ac.uk/2022-12-15T07:28:49+00:00Applications of Artificial Intelligence in Battling against Covid-19: A Literature Review2022-12-15T07:28:49+00:002022-12-15T07:28:49+00:00Mohammad Tayarani-Najarantag:biocomputation.herts.ac.uk,2022-12-15:/2022/12/15/applications-of-artificial-intelligence-in-battling-against-covid-19-a-literature-review.html<p class="first last">Mohammad Tayarani-Najaran's Journal Club session where he will talk about a paper "Applications of Artificial Intelligence in Battling against Covid-19: A Literature Review"</p>
<p>This week on Journal Club session Mohammad Tayarani-Najaran will talk about a paper "Applications of Artificial Intelligence in Battling against Covid-19: A Literature Review".</p>
<hr class="docutils" />
<p>Colloquially known as coronavirus, the Severe Acute Respiratory Syndrome
CoronaVirus 2 (SARS-CoV-2), that causes CoronaVirus Disease 2019 (COVID-19),
has become a matter of grave concern for every country around the world. The
rapid growth of the pandemic has wreaked havoc and prompted the need for
immediate reactions to curb the effects. To manage the problems, many research
in a variety of area of science have started studying the issue. Artificial
Intelligence is among the area of science that has found great applications in
tackling the problem in many aspects. Here, we perform an overview on the
applications of AI in a variety of fields including diagnosis of the disease
via different types of tests and symptoms, monitoring patients, identifying
severity of a patient, processing covid-19 related imaging tests, epidemiology,
pharmaceutical studies, etc. The aim of this paper is to perform a
comprehensive survey on the applications of AI in battling against the
difficulties the outbreak has caused. Thus we cover every way that AI
approaches have been employed and to cover all the research until the writing
of this paper. We try organize the works in a way that overall picture is
comprehensible. Such a picture, although full of details, is very helpful in
understand where AI sits in current pandemonium. We also tried to conclude the
paper with ideas on how the problems can be tackled in a better way and provide
some suggestions for future works.</p>
<div class="line-block">
<div class="line"><br /></div>
</div>
<p>Papers:</p>
<ul class="simple">
<li>M. Tayarani, N., <a class="reference external" href="https://doi.org/10.1016/j.chaos.2020.110338">"Applications of Artificial Intelligence in Battling against Covid-19: A Literature Review"</a>, 2021, Chaos, Solitons & Fractals, 142, 110338</li>
</ul>
<p><strong>Date:</strong> 2022/12/16 <br />
<strong>Time:</strong> 14:00 <br />
<strong>Location</strong>: online</p>
Explanation in Human-AI Systems2022-07-13T22:36:16+01:002022-07-13T22:36:16+01:00Epaminondas Kapetaniostag:biocomputation.herts.ac.uk,2022-07-13:/2022/07/13/explanation-in-human-ai-systems.html<p class="first last">Epaminondas Kapetanios's Journal Club session where he will talk about his editorial work in the "Explanation in Human-AI Systems" journal.</p>
<p>This week on Journal Club session Epaminondas Kapetanios will talk about his editorial work in the "Explanation in Human-AI Systems" journal. Please see the journal description below for more details.</p>
<hr class="docutils" />
<p>Intelligent systems and applications, mainly Machine Learning (ML) based
Artificial Intelligence (AI), have been employed at almost all levels and in
all domains of society: from AI systems in agriculture (e.g., greenhouse
optimization) to algorithm-based trading in finance as well as in our personal
companions such as social robots and personal voice assistants (e.g., Siri,
Alexa). Concerns, however, have been raised on the grounds of their -
transparency, safety and liability, algorithmic bias and fairness, and
trustworthiness. In response to these concerns, regulatory frameworks governed
by AI principles in society have emerged at both, institutional and
governmental levels. In addition, a response from Artificial Intelligence and
Machine Learning (AI/ML) communities has emerged in the form of interpretable
models and Explainable AI (XAI) tools and approaches. However, these come with
limitations in explaining the behavior of complex AI/ML systems to technically
inexperienced users.</p>
<p>This Research Topic focuses on how to conceptualize, design, and implement
human-AI systems that can explain their decisions and actions to different
types of consumers and personas. Current approaches in Machine Learning are
tailored more towards interpretations and explanations that are more suitable
for modelers and less for technically inexperienced users. In other human-AI
interactions, for instance, Google Assistance, Alexa, Social Robots, Web
search, and recommendation systems, explanations for recommendations, search
results, or actions are not even considered as an integral part of the human-AI
interaction mechanism. As a result, there is a need to revisit the
conceptualization, design, and implementation of human-AI systems in a way that
they provide more transparency in their way of reasoning and how they
communicate this via adaptive explanation techniques for different types of
users. This can be better achieved by taking a cross-disciplinary approach to
the concept of “explanation” and views of “what is a good explanation”. For
instance, disciplines such as philosophy and psychology of science (e.g.,
theory of explanation, causal reasoning), social sciences (e.g., social
expectations), psychology (e.g., cognitive bias), communication, and media
science offer an intellectual basis of what ‘explanation’ is and how to do
people select, to evaluate, and communicate explanations.</p>
<p>This Research Topic invites researchers and practitioners from academic
institutions and private companies to submit their articles on the
conceptualization, design, and implementation of explainable human-AI systems
from a theoretical/systemic, and practical standpoint.</p>
<div class="line-block">
<div class="line"><br /></div>
</div>
<p>Links:</p>
<ul class="simple">
<li><a class="reference external" href="https://www.frontiersin.org/research-topics/20958/explanation-in-human-ai-systems#overview">"Explanation in Human-AI Systems"</a>.</li>
</ul>
<p><strong>Date:</strong> 2022/07/15 <br />
<strong>Time:</strong> 14:00 <br />
<strong>Location</strong>: online</p>
A Spiking Neural Program for Sensorimotor Control during Foraging in Flying Insects2021-06-23T11:28:03+01:002021-06-23T11:28:03+01:00Shavika Rastogitag:biocomputation.herts.ac.uk,2021-06-23:/2021/06/23/a-spiking-neural-program-for-sensorimotor-control-during-foraging-in-flying-insects.html<p class="first last">Shavika Rastogi's Journal Club session where she will talk about a paper "A Spiking Neural Program for Sensorimotor Control during Foraging in Flying Insects"</p>
<p>This week on Journal Club session Shavika Rastogi will talk about a paper "A Spiking Neural Program for Sensorimotor Control during Foraging in Flying Insects".</p>
<hr class="docutils" />
<p>Foraging is a vital behavioral task for living organisms. Behavioral
strategies and abstract mathematical models thereof have been
described in detail for various species. To explore the link between
underlying neural circuits and computational principles, we present
how a biologically detailed neural circuit model of the insect
mushroom body implements sensory processing, learning, and motor
control. We focus on cast and surge strategies employed by flying
insects when foraging within turbulent odor plumes. Using a spike-
based plasticity rule, the model rapidly learns to associate
individual olfactory sensory cues paired with food in a classical
conditioning paradigm. We show that, without retraining, the system
dynamically recalls memories to detect relevant cues in complex
sensory scenes. Accumulation of this sensory evidence on short time
scales generates cast-and-surge motor commands. Our generic systems
approach predicts that population sparseness facilitates learning,
while temporal sparseness is required for dynamic memory recall and
precise behavioral control. Our work successfully combines biological
computational principles with spike-based machine learning. It shows
how knowledge transfer from static to arbitrary complex dynamic
conditions can be achieved by foraging insects and may serve as
inspiration for agent-based machine learning.</p>
<div class="line-block">
<div class="line"><br /></div>
</div>
<p>Papers:</p>
<ul class="simple">
<li>H. Rapp, M. Nawrot, <a class="reference external" href="https://doi.org/10.1073/pnas.2009821117">"A Spiking Neural Program for Sensorimotor Control during Foraging in Flying Insects"</a>, 2020, Proceedings of the National Academy of Sciences, 117, 28412--28421</li>
</ul>
<p><strong>Date:</strong> 2021/06/25 <br />
<strong>Time:</strong> 14:00 <br />
<strong>Location</strong>: online</p>
Reader/Principal Lecturer in Computer Science (Artificial Intelligence)2021-04-09T22:56:49+01:002021-04-09T22:56:49+01:00Volker Steubertag:biocomputation.herts.ac.uk,2021-04-09:/2021/04/09/reader-principal-lecturer-in-computer-science-artificial-intelligence-.html<p class="first last">Applications are invited for an academic position as Reader/Principal Lecturer in the Department of Computer Science, University of Hertfordshire. The Department has an international reputation for teaching and research, with 64 academic staff, 40 adjunct lecturer staff, and 65 research students and postdoctoral research staff. With a history going back to 1958, the Department teaches one of the largest cohorts of undergraduate students in the UK, and also delivers a thriving online computer science degree programme.</p>
<p>School of Physics Engineering and Computer Science/ Department of Computer Science</p>
<p>University of Hertfordshire, Hatfield, UK</p>
<p>FTE: Full time position working 37 hours per week (1.0 FTE)</p>
<p>Duration of Contract: Permanent</p>
<p>Salary: UH9 £51,034 - £60,905 pa dependent on relevant skills and experience</p>
<p><strong>Closing date: 9 May 2021</strong></p>
<hr class="docutils" />
<p>Applications are invited for an academic position as Reader/Principal Lecturer in the Department of Computer Science, University of Hertfordshire. The Department has an international reputation for teaching and research, with 64 academic staff, 40 adjunct lecturer staff, and 65 research students and postdoctoral research staff. With a history going back to 1958, the Department teaches one of the largest cohorts of undergraduate students in the UK, and also delivers a thriving online computer science degree programme.</p>
<hr class="docutils" />
<div class="section" id="main-duties-and-responsibilities">
<h2>Main duties and responsibilities</h2>
<p>The person appointed will be expected to make a significant contribution to the leadership of research in the department, including gaining research awards as Principal Investigator, the development of the research environment in the department and across the University, and publishing in peer reviewed journal articles and other internationally excellent or world-leading publications in education. To contribute to the development of, and supervise and teach on, doctoral programmes in the UK and internationally in relation to a wide spectrum of AI, especially emerging topics in AI. <strong>Possible fields include, but are not limited to:</strong></p>
<ul class="simple">
<li><strong>Machine learning</strong>: reinforcement learning, Deep Methods, statistical methods, large scale data modelling/intelligent processing and high-performance learning algorithms</li>
<li><strong>Robotics</strong>: embodied and/or cognitive robotics, HRI, robot safety, emotional/social robots, smart homes and sensors, sensor fusion, assistive robotics, soft robotics, adaptive or evolutionary robotic design</li>
<li><strong>Biological and biophysical computation paradigms, systems biology, neural computation</strong></li>
<li><strong>Complex Systems</strong>: collective intelligence, adaptive, autonomous and multi-agent/robot systems, collective and swarm intelligence, social and market modelling, adaptive, evolutionary and unconventional computation</li>
<li><strong>Mathematical Modelling</strong>: statistical modelling, information-theoretic methods, compressive sensing, intelligent data visualization, multiscale models, optimization; causality</li>
<li><strong>Emerging Topics in AI</strong>: computer algebra and AI, topological methods (e.g. persistent homology), algebraic and category-theoretical methods in AI; modern topics in games and AI; quantum algorithms for AI</li>
<li><strong>AI and applications</strong>: financial modelling, AI and biology/physics/cognitive sciences</li>
<li><strong>Foundations</strong>: fundamental questions of intelligence and computation, emergence of life/intelligence, Artificial Life</li>
</ul>
<p>Preference will be given to candidates that can deliver teaching to Level 7 in a selection of relevant subjects.</p>
<p>The appointee will also be expected to lead and develop taught modules in a range of computer science areas. For appointees with the appropriate experience, there will be the possibility of taking up the role of Head of Subject Group within the department of Computer Science.</p>
</div>
<div class="section" id="skills-and-experience">
<h2>Skills and experience</h2>
<p>The appointee will strengthen the research culture in the Department by pursuing research as part of a larger research team, seeking external funding, publishing papers, supervising research students, and participating in commercial activity as appropriate. Therefore it is essential that candidates have a track record (e.g. in published, grant-funded research) in Computer Science. Additionally, experience of different types of assessment and higher education quality assurance is an essential requirement of this role.</p>
<p>Prior experience of developing modules and/or programmes of study in Computer Science is essential in addition to significant experience of operating in a UK HE Environment, or equivalent professional experience. Readers/Principal lecturers are expected to take on duties in the capacity of leader, and hence experience of academic leader, programme leadership and line management is desirable. Good interpersonal and presentation skills with proficiency in the English Language are essential along with the ability to manage conflicting demands and work to deadlines.</p>
</div>
<div class="section" id="qualifications-required">
<h2>Qualifications required</h2>
<p>Reader/Principal Lecturer applicants must hold a First Degree and a PhD in an appropriate area of Computer Science or an equivalent, relevant postgraduate professional qualifications.</p>
<p>In addition, the Reader/Principal Lecturer will be expected to contribute to the leadership and management academic programmes, as well as proactive participation in enterprise, knowledge transfer and/or research and scholarship in the School. There are expectations of leadership and potentially supervisory oversight of groups of staff. Readers/Principal lecturers are also expected to contribute to the richness of the academic environment, through scholarly activity, support events, projects and activities, including open days, outreach, extra curricula initiatives, and potentially act as a representative of the School or University at national or international fora.</p>
<p>The School of Physics, Engineering and Computer Science is an Athena Swan Bronze award holder, and we are committed to providing a supportive environment and flexible working arrangements. The university also provides an onsite childcare facility and child-centred holiday clubs. Staff work with the university values, which are: Friendly, Ambitious, Collegial, Enterprising, and Student focused.</p>
<p><strong>Contact Details/Informal Enquiries</strong>: Informal enquiries may be addressed to Dr Simon Trainis, Head, Department of Computer Science by email: <code>S.A.Trainis [at] herts.ac.uk</code> Please note that applications sent directly to this email address will not be accepted.</p>
<p><strong>Closing Date</strong>: 9 May 2021</p>
<p>Interview Dates: TBC but candidates are advised to be available on 16 and 17 June 2021</p>
<p><strong>Apply</strong> through <a class="reference external" href="https://www.herts.ac.uk/staff/careers-at-herts">https://www.herts.ac.uk/staff/careers-at-herts</a>, Reference Number: 032595</p>
<p>Date Advert Placed: 8 April 2021</p>
</div>
Dynamic Hierarchical Structure for Cloud Computing Job Scheduling Utilizing Artificial Intelligence Technologies2020-02-12T13:25:05+00:002020-02-12T13:25:05+00:00Emil Dmitruktag:biocomputation.herts.ac.uk,2020-02-12:/2020/02/12/dynamic-hierarchical-structure-for-cloud-computing-job-scheduling-utilizing-artificial-intelligence-technologies.html<p class="first last">Na Helian's journal club session where she will talk about the paper "Dynamic Hierarchical Structure for Cloud Computing Job Scheduling Utilizing Artificial Intelligence Technologies".</p>
<p>This week on Journal Club session Na Helian will talk about the paper "Dynamic Hierarchical Structure for Cloud Computing Job Scheduling Utilizing Artificial Intelligence Technologies".</p>
<hr class="docutils" />
<p>Cloud computing is widely used due to its cost effectiveness and starvation
free execution of processes. There has been substantial research done
in job scheduling algorithm in cloud computing to improve scheduling
performance, but little attention has been paid to structure design for
job scheduling. This paper aims to improve job scheduling makespan (max
processing time for given jobs) in a cloud environment. A dynamic
hierarchical structure, which introduces sub-schedulers between scheduler
and servers, is proposed to dynamically change the connection pattern
between sub-schedulers and servers by using artificial intelligence search
algorithms. Due to its dynamic and flexible nature, this design enables
the system to adaptively accommodate the heterogeneity of jobs and resources
in order to make most use of the resources available. Experimental results
demonstrate that a dynamic hierarchical structure can significantly reduce
the total makespan of the heterogeneous tasks allocated to heterogeneous
resources, compared with a one-layer structure. This reduction is
particularly pronounced when resources are scarce.</p>
<p>Papers:</p>
<ul class="simple">
<li>"Dynamic Hierarchical Structure for Cloud Computing Job Scheduling
Utilizing Artificial Intelligence Technologies"</li>
</ul>
<div class="line-block">
<div class="line"><br /></div>
</div>
<p><strong>Date:</strong> 14/02/2020 <br />
<strong>Time:</strong> 16:00 <br />
<strong>Location</strong>: B200</p>
Towards Discriminative representation learning for speech Emotion recognition2020-01-29T11:03:39+00:002020-01-29T11:03:39+00:00Emil Dmitruktag:biocomputation.herts.ac.uk,2020-01-29:/2020/01/29/towards-discriminative-representation-learning-for-speech-emotion-recognition.html<p class="first last">Yi Sun's journal club session where he will talk about the paper "Towards Discriminative representation learning for speech Emotion recognition".</p>
<p>This week on Journal Club session Yi Sun will talk about the paper "Towards Discriminative representation learning for speech Emotion recognition".</p>
<hr class="docutils" />
<p>In intelligent speech interaction, automatic speech emotion recognition (SER)
plays an important role in understanding user intention. While sentimental
speech has different speaker characteristics but similar acoustic attributes,
one vital challenge in SER is how to learn robust and discriminative
representations for emotion inferring. In this paper, inspired by human
emotion perception, we propose a novel representation learning component (RLC)
for SER system, which is constructed with Multi-head Self-attention and Global
Context-aware Attention Long Short-Term Memory Recurrent Neutral Network
(GCA-LSTM). With the ability of Multi-head Self-attention mechanism in
modeling the element-wise correlative dependencies, RLC can exploit the
common patterns of sentimental speech features to enhance emotion-salient
information importing in representation learning. By employing GCA-LSTM,
RLC can selectively focus on emotion-salient factors with the consideration
of entire utterance context, and gradually produce discriminative representation
for emotion inferring. Experiments on public emotional benchmark database
IEMOCAP and a tremendous realistic interaction database demonstrate the
outperformance of the proposed SER framework, with 6.6% to 26.7% relative
improvement on unweighted accuracy compared to state-of-the-art techniques.</p>
<p>Papers:</p>
<ul class="simple">
<li>Runnan Li et al. (2019) <a class="reference external" href="https://www.ijcai.org/Proceedings/2019/703">"Towards Discriminative Representation Learning for Speech Emotion Recognition"</a> ,
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligenced, Main track, Pages 5060-5066</li>
</ul>
<div class="line-block">
<div class="line"><br /></div>
</div>
<p><strong>Date:</strong> 31/01/2020 <br />
<strong>Time:</strong> 16:00 <br />
<strong>Location</strong>: B200</p>
ArchaeaBot: A Post Singularity, Post Climate Change Lifeform2018-04-13T13:28:10+01:002018-04-13T13:28:10+01:00Anna Dumitriu and Alex Maytag:biocomputation.herts.ac.uk,2018-04-13:/2018/04/13/archaeabot-a-post-singularity-post-climate-change-lifeform.html<p class="first last">Anna Dumitriu and Alex May's journal club session on "ArchaeaBot: A Post Singularity, Post Climate Change Lifeform".</p>
<p>Artists in Residence Anna Dumitriu and Alex May were recently awarded an European Media Art Platform Residency at LABoral in Spain and Arts Council England funding to develop their new project in progress. The work will take the form of underwater robotic art installation that explores what 'life' might mean in a post singularity, post climate change future. The robots will be based on new research about archaea (the most ancient life forms on earth which often live in extreme environments) combined with the latest innovations in machine learning & artificial intelligence to create the 'ultimate' species for the end of the world as we know it. The project involves collaboration with Imperial College and the University of Hertfordshire.</p>
<p><strong>Date:</strong> 20/04/2018 <br />
<strong>Time:</strong> 16:00 <br />
<strong>Location</strong>: LB252</p>
Optimising hierarchical load balancing for the Cloud2016-10-13T11:14:35+01:002016-10-13T11:14:35+01:00Paul Moggridgetag:biocomputation.herts.ac.uk,2016-10-13:/2016/10/13/optimising-hierarchical-load-balancing-for-the-cloud.html<p class="first last">Paul Moggridge introduces his research on optimising hierarchical load balancing for the cloud in this week's journal club session.</p>
<p>In the first half of the presentation we will cover the evolution and characteristics of the cloud computing. We will look in particular at the different existing algorithms for load balancing in the cloud. In second half we will see where my research topic fits in along with the challenges and significance of my research.</p>
<p><strong>Date:</strong> 14/10/2016 <br />
<strong>Time:</strong> 16:00 <br />
<strong>Location</strong>: LB252</p>
The potential for using artificial intelligence techniques to improve e-Learning systems2016-04-06T17:40:11+01:002016-04-06T17:40:11+01:00Edward Wakelamtag:biocomputation.herts.ac.uk,2016-04-06:/2016/04/06/the-potential-for-using-artificial-intelligence-techniques-to-improve-e-learning-systems.html<p class="first last">Edward Wakelam's journal club session on the use of artificial intelligence techniques to improve e-Learning systems.</p>
<p>There has been significant progress in the development of techniques to deliver more effective e-Learning systems in both education and commerce but there are very few examples of comprehensive learning systems that exploit contemporary artificial intelligence (AI) techniques. We have surveyed existing intelligent learning/training systems and explored the contemporary AI techniques which appear to offer the most promising contributions to e-Learning. With the convergence of several of the required components for success increasingly in place the opportunity to make progress now appears to be much stronger.</p>
<p>In the field of education, the mining, extraction and exploitation of useful information and patterns from student data provides lecturers, trainers and organisations with the potential to tailor learning paths and materials to maximize teaching efficiency and to predict and influence student success rates. Progress in the area of student data analytics can provide useful techniques for exploitation in the development of adaptive learning systems. Student data often includes a combination of nominal and numeric data. A large variety of techniques are available to analyse numeric data, however there are fewer techniques applicable to nominal data. We have explored potential correlations between student attributes in a freely available data set, including the application of what we believe to be a novel technique to analyse nominal data, providing the opportunity to focus on promising correlations for deeper analysis.</p>
<p><strong>Date:</strong> 8/04/2016 <br />
<strong>Time:</strong> 16:00 <br />
<strong>Location</strong>: LB252</p>