Aladdin Ayesh

Personal profile

My prime research area of interest within Artificial Intelligence is Knowledge Representation and Reasoning in Cognitive Systems. This falls within the crossing boundaries of Knowledge Engineering and Intelligent Agents.

My current research focuses on Emotions Modelling, Social Swarms, and Text Mining with their applications in knowledge management, ad hoc networks, eLearning and games.

Some of the research outcome can be seen on the showcase channel: http://uk.youtube.com/user/aayesh15

Connect to me via LinkedIn

Research group affiliations

Mobile Cognitive Systems (MCS)

Software Technology Research Laboratory (STRL)

Institute of Artificial Intelligence (IAI)

Publications and outputs

Continuous Stress Monitoring under Varied Demands Using Unobtrusive Devices
Continuous Stress Monitoring under Varied Demands Using Unobtrusive Devices Lim, Yee Mei; Ayesh, Aladdin, 1972-; Stacey, Martin Kenneth This research aims to identify a feasible model to predict a learner’s stress in an online learning platform. It is desirable to produce a cost-effective, unobtrusive and objective method to measure a learner’s emotions. The few signals produced by mouse and keyboard could enable such solution to measure real world individual’s affective states. It is also important to ensure that the measurement can be applied regardless the type of task carried out by the user. This preliminary research proposes a stress classification method using mouse and keystroke dynamics to classify the stress levels of 190 university students when performing three different e-learning activities. The results show that the stress measurement based on mouse and keystroke dynamics is consistent with the stress measurement according to the changes of duration spent between two consecutive questions. The feedforward back-propagation neural network achieves the best performance in the classification. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
Behavior-Based Outlier Detection for Network Access Control Systems
Behavior-Based Outlier Detection for Network Access Control Systems Muhammad, Musa Abubakar; Ayesh, Aladdin, 1972-; Wagner, Isabel Network Access Control (NAC) systems manage the access of new devices into enterprise networks to prevent unauthorised devices from attacking network services. The main difficulty with this approach is that NAC cannot detect abnormal behaviour of devices connected to an enterprise network. These abnormal devices can be detected using outlier detection techniques. Existing outlier detection techniques focus on specific application domains such as fraud, event or system health monitoring. In this paper, we review attacks on Bring Your Own Device (BYOD) enterprise networks as well as existing clustering-based outlier detection algorithms along with their limitations. Importantly, existing techniques can detect outliers, but cannot detect where or which device is causing the abnormal behaviour. We develop a novel behaviour-based outlier detection technique which detects abnormal behaviour according to a device type profile. Based on data analysis with K-means clustering, we build device type profiles using Clustering-based Multivariate Gaussian Outlier Score (CMGOS) and filter out abnormal devices from the device type profile. The experimental results show the applicability of our approach as we can obtain a device type profile for five dell-netbooks, three iPads, two iPhone 3G, two iPhones 4G and Nokia Phones and detect outlying devices within the device type profile.
A Behaviour Profiling Based Technique for Network Access Control Systems
A Behaviour Profiling Based Technique for Network Access Control Systems Muhammed, Musa Abubaker; Ayesh, Aladdin, 1972- The emergence of the Internet of Things (IoT) has lead to a tech- nological transformation that integrates several technologies to represent the future of computing and communications. IoT is the interconnection of internet of computing devices embedded in objects such as sensors, actuators and networks enabling them to send and receive data. Bring Your Own Device (BYOD) is an integral part of IoT, which is the practice of allowing em- ployees to use their own smartphones, tablets and laptops in the workplace to access enterprise resources and applications. It of- fers several benefits like employee job satisfaction, productivity, increased job efficiency and flexibility. With all the advantages offered, BYOD environments are still less safe because of per- sistent security threats, attacks caused by loss, theft of personal devices and corporate data leakage. BYOD enterprise network is accessed through enterprise mobility management solutions which monitor, controls and enforce access control policies to devices accessing the network. This paper aims to close the gap of the existing Network Access Control (NAC) systems focusing on 802.1x protocols with a Novel Device type Behaviour Profiling Technique. The Behaviour Profiling Technique used a dataset proposed in [29] to develop a device type behaviour profiles of five dell netbooks, three iPads, two iPhones 3G, two iPhones 4G and two Nokia Phones using K-Means Clustering Algorithm. open access journal
Big Data, Artificial Intelligence and Robotics
Big Data, Artificial Intelligence and Robotics Lahiri, Indrani; Ayesh, Aladdin, 1972-; Mills, Simon Call for papers We invite contributions to a Special Issue on Big Data, AI and Digital Futures: Challenges, changes and continuities, to be published by the AI & Society Journal of Culture, Knowledge and Communication (Springer) http://link.springer.com/journal/146. This special issue arises from the Big Data, AI and Robotics (BDAIR 18) Research Symposium at the De Montfort University (DMU). The main objective of this special issue is to encourage cross-disciplinary, interdisciplinary research and international research collaboration. ============================= SPECIAL ISSUE THEMES ============================= Aims: The aim of this special issue is to address the societal complex issues emerging from the recent advances in AI. As we look for answers to address new challenges, we look at the impact of AI and big data on our futures in the context of society. Content How might algorithms and big data shape our digital futures? In what ways can the semantic web impact our everyday life? Are there ways of envisioning a structure for managing data in a meaningful way, which may offer a transformational experience? We are witnessing a shift in political, social, cultural and technical relations which are increasingly driven by big data and algorithms. Our external environment is being codified leading to an increased level of surveillance both at personal and professional levels. This in itself is a challenge to privacy and data protection. We are already experiencing self-monitoring and tracking with the devices we wear that prompt us to engage in certain behaviours. Are we far from a day when technology will induce behavioural changes, not only at cognitive level but also at conative levels? What for claims that Big Data will make theory redundant? What ontological and epistemological issues arise in relation to these technologies? Our thoughts, emotions and actions are increasingly getting interpellated by algorithms and data. How does that then impact on the ‘Logos-Pathos-Ethos’ of our lives? Sophia bot froze on the question of corruption in Ukraine. On the other hand, we witnessed “the great British Brexit robbery” (Guardian, 2017) that proved whoever owns the data actually wins the campaign, election and the world. Cambridge Analytics Brexit has been one of the popular searches on the internet. At the same time, big data pose challenges as they generate noise and that means data often can be indecipherable, bewildering and recherché. Disruptions are common when we deal with data in any subject area. Therefore, it is cardinal to address the technological complexity, not only through academic research, scholarship and pedagogic practice but also industry engagement. On the other hand, big data and algorithms embed innovation and we encounter technologies in a transformational way, where conversations and dialogic interventions are rapid. Perhaps due to the contrasting ways in which we engage with big data and algorithms, the need for well-defined theoretical frameworks and methodological tools are increasingly in demand Siapera, 2018). Readership National and International We will invite experts both nationally and internationally to contribute to this special issue Goal Our goal is to offer an interdisciplinary coverage of the area explored, by bringing together perspectives from different domains such as computer science, design studies, business, cultural anthropology, arts and humanities and social sciences. In particular, we welcome contributions that explore the following themes: Themes Topics include, but are not limited to, the following: Media datafication and neoliberalism Data and business Social media and big data Big data, PR and Advertising Big data and politics Ethics, privacy and technology Data and sustainability Personalisation, Machine learning and AI Social bots and the management of sociality Quantified self and data cultures Data and education Researching media and culture using data methods Data visualisation, art and design Social responsibility and innovation Data and health Mobile and locative media Data and surveillance Using Big Data to test social theories Social data collection and novelty
An Outcome based approach to developing a Belarusian Qualification Framework
An Outcome based approach to developing a Belarusian Qualification Framework Gatward, R.; Moemeni, A.; Ayesh, Aladdin, 1972-; Lebegue, P.; Caillier, A.; Rudniewski, J.; Repca, M. The Higher Education landscape of Belarus is characterised by high quality institutions offering world class expertise and facilities, and a very high participation rate in higher education. However, it has also been recognised by the state that the degree of individuality and autonomy prevalent in these institutions works against the current mood of globalisation in Higher Education. An obvious example is international exchanges. It is particularly difficult in the case of students since the programmes are usually organised in an insular way and lack a precise specification of the level at which any contributory course is delivered. A stated objective of the Belarusian Ministry of Education is to seek membership of the European Higher Education Area (EHEA). To this end a road map (Eastern Partnership Civil Society Forum, 2017), designed to afford increased international compatibility of the Belarusian Higher Education Framework, has been defined and is being implemented by the Belarusian Ministry of Education. This paper considers how the EU funded project IESED could directly contribute to the realisation of this Road Map. http://iesed.esy.es/
Improving Security in Bring Your Own Device (BYOD) Environment by Controlling Access
Improving Security in Bring Your Own Device (BYOD) Environment by Controlling Access Muhammad, M.A.; Zadeh, P.B.; Ayesh, Aladdin, 1972- With the rapid increase in smartphones and tablets, Bring Your Own Devices (BYOD) has simplified computing by introducing the use of personally owned devices. These devices can be utilised in accessing business enterprise contents and networks. The effectiveness of BYOD offers several business benefits like employee job satisfaction, increased job efficiency and flexibility. However, allowing employees to bring their own devices could lead to a plethora of security issues; like data theft, unauthorised access and data leakage. This paper investigates the current security approaches and how organisations can leverage on these techniques regarding policies, risks and existing security techniques to mitigate or halt the security challenges. This research aimed to fill up the access control gap in the BYOD environment by developing an Intelligent Filtering Technique (IFT) using Artificial Intelligence (AI) Technique. Based on the behavioural patterns of device packets Inter-Arrival-Time (IAT) features through network traffic flow packet headers (Such as Transmission Control Protocol (TCP), User Datagram Protocol (UDP) and Internet Control Messaging Protocol (ICMP)).
Combining Supervised and Unsupervised Learning to Discover Emotional Classes
Combining Supervised and Unsupervised Learning to Discover Emotional Classes Santos, O.C.; Ayesh, Aladdin, 1972-; Arevalillo-Herraez, M.; Arnau-Gonzalez, P. Most previous work in emotion recognition has fixed the available classes in advance, and attempted to classify samples into one of these classes using a supervised learning approach. In this paper, we present preliminary work on combining supervised and unsupervised learning to discover potential latent classes which were not initially considered. To illustrate the potential of this hybrid approach, we have used a Self-Organizing Map (SOM) to organize a large number of Electroencephalogram (EEG) signals from subjects watching videos, according to their internal structure. Results suggest that a more useful labelling scheme could be produced by analysing the resulting topology in relation to user reported valence levels (i.e., pleasantness) for each signal, refining the original set of target classes.
The effects of typing demand on learner's Motivation/Attitude-driven Behaviour (MADB) model with mouse and keystroke behaviours
The effects of typing demand on learner's Motivation/Attitude-driven Behaviour (MADB) model with mouse and keystroke behaviours Lim, Yee Mei; Ayesh, Aladdin, 1972-; Stacey, Martin It would be desirable to have an automated means of assessing a learner's motivation and stress levels in an e-learning system, which would give impact on his or her learning performance. This preliminary research examines the effects of typing task demand on Motivation/Attitude-driven Behavior (MADB) model. The model is adapted from what was proposed by Wang [1], which is used to describe how the motivation process drives human behaviours and actions, and how the attitude and decision-making process help to regulate and determine the action to be taken by the learner. The effects of typing demand are tested on learners' stress perceptions, motivation, attitudes, decision, as well as their mouse and keystroke behaviours. The typing demand is varied by the pre-defined text length and language familiarity. The results of Multivariate Analysis of Variance and correlation tests are generally congruent with the MADB model proposed by Wang, but with minor difference. We also found that a learner's behaviour is significantly correlated to his or her mouse and keystroke behaviours. A revised version of MADB model based on e-learning environment is proposed. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
Class discovery from semi-structured EEG data for affective computing and personalisation
Class discovery from semi-structured EEG data for affective computing and personalisation Ayesh, Aladdin, 1972-; Arevalillo-Herraez, M.; Amau-Gonzalez, P. Many approaches to recognising emotions from metrical data such as EEG signals rely on identifying a very small number of classes and to train a classifier. The interpretation of these classes varies from a single emotion such as stress [24] to features of emotional model such as valence-arousal [4]. There are two major issues here. First classification approach limits the analysis of the data within the selected classes and is also highly dependent on training data/cycles, all of which limits generalisation. Second issue is that it does not explore the inter-relationships between the data collected missing out on any correlations that could tell us interesting facts beyond emotional recognition. This second issue would be of particular interest to psychologists and medical professions. In this paper, we investigate the use of Self-Organizing Maps (SOM) in identifying clusters from EEG signals that could then be translated into classes. We start by training varying sizes of SOM with the EEG data provided in a public dataset (DEAP). The produced graphs showing Neighbour Distance, Sample Hits, Weight Position are analysed holistically to identify patterns in the structure. Following that, we have considered the ground- truth label provided in DEAP, in order to identify correlations between the label and the clustering produced by the SOM. The results show the potential of SOM for class discovery in this particular context. We conclude with a discussion on the implications of this work and the difficulties in evaluating the outcome. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
Conceptual Motivation Modeling for Students with Dyslexia for enhanced Assistive Learning
Conceptual Motivation Modeling for Students with Dyslexia for enhanced Assistive Learning Wang, Ruije; Chen, Liming; Solheim, Ivar; Schulz, Trenton; Ayesh, Aladdin, 1972- Students with dyslexia often suffer from the lack of academic self-worth and frustration that can even lead to learned helplessness. However, few studies have investigated the impact of incorporating users’ motivational factors into user modeling to enhance the learning experience for students with dyslexia. In this paper we attempt to develop a conceptual motivation model for people with dyslexia in their use of assistive learning tools. To this end, we carry out a small-scale empirical study in real-world learning scenarios. Then we conduct individual interviews to gain first-hand data about the key factors and features affecting learning experience. Using coding and thematic analysis methods we discuss main themes regarding motivational factors and their interrelationships identified. Based on these findings, we create a conceptual motivation model tailored towards students with dyslexia, which will help enhance learning experience and improve learning efficiency.

Click here to view a full listing of Aladdin Ayesh's publications and outputs.

Key research outputs

AI planning, knowledge representation, logic
Cognitive Robots and cognitive systems
Distributed AI (DAI) and multi-agents architectures
Neural nets and neuro-science
Emerging technologies.

Research interests/expertise

Artificial Intelligence
Agent-Oriented Systems
Cognitive Systems
Swarm Intelligence
Natural Language Processing
Text Mining
Knowledge Representation and Engineering
Sensor Networks and Data Fusion
Intelligent Interfaces
Multimedia and Multi-modal systems.

Areas of teaching

Game Programming
Robotics
Software Engineering
Multi-agent Systems
Multimedia.

Qualifications

Academic Qualifications

2000 PhD in Computer Science, Liverpool John Moores University, Liverpool, UK
1995 MSc. in Computer Science (AI), Essex University, Colchester, UK

Professional Qualifications

2009 Senior Member of IEEE (SMIEEE)
2009 Fellow of British Computer Society (FBCS)
2007 Fellow of the Higher Education Academy (FHEA)
2006 Chartered Engineer (CEng), Engineering Council UK
2004 Charted IT Practitioner (CITP), British Computer Society (BCS)

Courses taught

Undergraduates

Multimedia Architectures, Intro to Mobile Robotics, Fuzzy Logic and Knowledge Based Systems, Games Architectures and Design, Entertainment Games Computing and Robotics, Advanced Mobile Robotics, Multimedia Animation Production, Electronic Publishing & Production III, Final Year Project

Postgraduates

Mobile Robots, Distributed Artificial Intelligence and Multi-Agent Systems, Intelligent Mobile Robots, Advanced Research Topics in Software Engineering, Research Methods for MA Creative Technology, Applied Research Methods, MSc Projects/Dissertations

PhD Training

Planning and Managing Your Research; a PhD training course: this is a compulsory training course for first year PhD students.

Membership of external committees

2019-now Steering Committee Member for the IEEE Transac- tions on Affective Computing (a IEEE Computer Society representative)
2018-now IEEE Technical Committee on Brain-inspired Cognitive Systems (TC-BCS).
2002-now IEEE SMC technical committee on Robotics and Intelligent Sensing

2010-2013 Elected member of BCS SIGAI committee
2009 -2011 Publication Officer of AISB
2008-2012 Elected member of the Society for the Study of Artificial Intelligence and Simulation of Behaviour (AISB) committee
2004-2010 BCS Leicestershire Branch YPG representative and local committee member (member since 1994)

Membership of professional associations and societies

British Computer Society (Chartered Fellow BCS)

Institute of Electric and Electronic Engineers (Senior Member IEEE)

IEEE Computer Society

IEEE Systems, Man and Cybernetics (SMC)

Association for Computing Machinery (ACM)

The Royal Institute of Philosophy (RIP)

The Society for the Study of Artificial Intelligence and Simulation of Behaviour (Senior Member AISB)

The Association for the Advancement of Artificial Intelligence (AAAI)

The Association for Computational Linguistics (ACL)

Professional licences and certificates

2009 Senior Member of IEEE

2009 Fellow of British Computer Society (FBCS/CITP)

2007 Fellow of the Higher Education Academy (FHEA)

2006 Chartered Engineer (CEng), Engineering Council UK

2006 Charted Scientist (CSci), The Science Council

2004 Charted IT Practitioner (BCS/CITP), British Computer Society (BCS)

Projects

Research Projects

[ITS-MathPS:2019, CI] Project Name/Description: Uso de Sistemas Tutoriales Inteligentes para Estu- diar Aspectos Cognitivos y Afectivos en la Ensen ̃anza Y El Aprendizaje de la Resolucion de Problemas Verbales Role: Co-Investigator - named external member, University of Valencia team (PI: Prof. Miguel Arevalillo Herraez) Source of Funding: Ministerio de Ciencia, Innovaci ́on y Universidades Total Budget: TBC

[Emotions-Valencia:2016, CI] Project Name/Description: Follow up - Emotions Modeling for eLearning Environments Role: Co-Investigator (Lead Researcher) Source of Funding: University of Valencia Inter- national Visiting Researchers Program Total Budget: e6500

[BIG-AFF:2015, CI] Project Name/Description: fusing multimodal BIG Data to provide low-Intrusive Affective and cognitive support in learning contexts (BIG-AFF) Role: Co-Investigator - named external member, University of Valencia team (PI: Prof. Francesc J. Ferri) Source of Funding: Ministerio de Economia Y Competitividad, Programa Estatal de Fomento de la Investigaci ́on C ́ıentifica y T ́ecnica de Ex- celencia Total Budget: e145,983

[Emotions-Valencia:2013, CI] Project Name/Description: Investigating Opportunities for Research Collab- oration - Emotions Modeling for eLearning Environments Role: Co-Investigator (Lead Researcher) Source of Funding: University of Valencia International Visiting Researchers Program (6500 Euro) Total Budget: £5530

[Learning-Andalucia:2010, CI] Project Name/Description: Design And Improvement Of Learning Algo- rithms Based On Soft Computing And It’s Application To Robot-Person Interaction, this project was lead by Prof. Antonio Gonz ́alez (PI) and involved 6 participants. Role: Co-Investigator Source of Funding: Grant No. P09-TIC04813, Proyecto de excelencia financiado por la Junta de Andaluc ́ıa, Spain, 2010-2014. Total Budget: £152,500 (183,000 Euro)

[AISB:2010, PI] Project Name/Description: AISB 2010 Convention Role: Principal Investigator Source of Funding: Conference fees and other associated income - 14 symposiums proposed, 10 ran, 197 attendees Value: £33,428

[TCIT-ONRG:2010, PI] Project Name/Description: Turing Test (TCIT-2010) Symposium – AISB2010 Role: Principal Investigator Source of Funding: The Office of Navy Research Global (ONRG) Value: £6442

[Portavata-EU:2010, PI] Project Name/Description: PortAvata: Portable Avatar for Virtual Presence Role: Principal Investigator Source of Funding: The Innovation Fellowships programme (funds provided by East Midlands Development Agency (EMDA) and European Regional Development Fund (ERDF) Value: £15,445

[Travel-RAE:2009, PI] Project Name/Description: RAE Conference Travel Grant to attend IEEE SMC 2009. Role: Principal Investigator Source of Funding: Royal Academy of Engineers - 2009 Value: £600

[SwarmStream–OHM:2008, PI] Project Name/Description: this is an open source project, currently in prealpha status on SourceForge.net and the code is being produced in collaboration with Georg-Simon-OHM Hochschule (Technical University) in Germany, where 26 students in 10 groups worked on producing base code of which 4 versions where chosen for release (2010). Role: Principal Investigator Source of Funding: Georg-Simon-OHM Hochschule (international visiting researchers/professors fund) Total Budget: £2207.80 (2660 Euro)

[Creativity-AHRC:2006, CI] Project Name/Description: Creativity Research Network: this project was led by Professor Stephen Brown (PI) and had number of participants. Role: Co-Investigator Source of Funding: AHRC Total Budget: £12,161

[RoboViz-EPSRC:2006, PI] Project Name/Description: RoboViz: Remote Command and Control for Robotic Emergent Behaviour – The Next Evolution of Telepresence: this project was in collaboration with University of Manchester, the funding was to enable DMU team to travel and use high end visualisation equipments at UoM. This was extended to utilize IOCT Robots. The project outcome was recorded in video (avaliable on youtube) and end of project technical report. Role: DMU Principal Investigator Source of Funding: EPSRC-CompuSteer Network Value: £1,480

[AIBO-NL:2006, CI] Project Name/Description: AIBO Stories: this project was part of the Narrative Labs which was led by Professor Sue Thomas (PI). I was leading on the AIBO stories activities. Role: Co- Investigator Source of Funding: HEIF funded under NLab: creativity and collaboration project. Total Budget: £30,000

[PhiOrch-RC:2004, CI] Project Name/Description: Philharmonia Orchestra Project: this project was a combination of research and industrial project. It was led by Professor Andrew Hugill (PI) and my participa- tion was mainly on the research side resulting in number of published papers. Role: Co-Investigator Source of Funding: Art Council 2003-2004 Value: £3,400

Infrastructural Projects

[IOCTRobots-SRIF:2005, CI] Project Name/Description: Equipment Funding – Institute of Creative Tech- nology Robots: this was part of a larger multi-faculties proposal submitted on behave of the University to the infrastructural funding scheme SRIF. I was leading on the robotics part of the proposal. Role: Co-Investigator (lead on the robotics) Source of Funding: SRIF 2005/2006 Total Budget: £481,000 Value: £150,000 for robotics

[TeachingRobots2-HEFECE:2003, PI] Project Name/Description: Equipment Funding – Mobile Robots Lab: this was the funding that launched the robotics lab, with more equipment such as AIBO dogs, to support the growth in robotics teaching and research. Role: Principal Investigator Source of Funding: HEFCE 2003/2004 Value: £47,000

[TeachingRobots1-DMU:2001, PI] Project Name/Description: Equipment Funding – Advanced Mobile Robots: this was a Faculty funding to support the development of the new module Advanced Mobile Robots, which I was developing at that time as a final year option. Role: Principal Investigator Source of Funding: School of Computing Fund 2001/2002 Value: £8,800

Consultancy and Industrial Projects

[FIM-APEng:2007, PI] Project Name/Description: FIM project Role: Principal Investigator Source of Funding: Funding in Kind from Ambient Performance Ltd. and Engenuity Technologies Inc. Total Budget: £2500 estimated value of contribution in terms of software licence and staff time from both companies.

[Consultancy-BAe:2002, CI] Project Name/Description: Consultancy Role: Co-Investigator Source of Funding: Company buyout 2002 Value: £2400 approx.

[Consultancy-GV:1999, PI] Project Name/Description: Consultancy Role: Principal Investigator Source of Funding: Company buyout 1999 Value: £3600

[TCS-Holage:2001, CI] Project Name/Description: Holage Management and Planning System - Teaching Company Scheme (TCS): this was a TCS partnership in collaboration with a holage company with a soft- ware development department. There were looking at developing their in-house software to a product that is available to other similar companies. Role: Co-Investigator Source of Funding: Teaching Company (UK) Scheme [Predecessor to KTP/InnovateUK]

Educational and Research into Teaching Projects

[GameEngine-TIF:2013, CI] Project Name/Description: The DMU Game Engine: this project was led by Dr. Ian Kenny (PI) and I provided the AI expertise. Role: Co-Investigator Source of Funding: Teaching Innovation Funding (TIF) - De Montfort University Value: £5000

[RoboCup-DMU:2008, PI] Project Name/Description: DMU RoboCup Team – Arena equipment: a team of master degree students created DMU AIBO Football team. Eventually we had two teams of robots playing against each other. The students then put a show at the Institute of Creative Technology (IOCT) building. My role was to guide the team technically and I used the project to introduce the students to some complex software code and advanced programming techniques. Role: Principal Investigator Source of Funding: Research Into Teaching Faculty/HEFCE RITI fund - 2007/08 Value: £500

[LegoAnts:2005, CI] Project Name/Description: 50 Lego RCX educational robots were purchased and utilised in teaching programming to HND students as part of innovative research into teaching initiative. Role: Co-Investigator, facilitated the purchasing of the robots with my colleague Mr. Clinton Ingram and assessed him in setting up the robots for the purpose of teaching programming using a visual programming language. A dedicated area in the HND lab were also set up. Source of Funding: Research Into Teaching Faculty Funding Value: £10,500

Funded Networks

[EUCog, 2013 -Now] Member of EUCog - European Network for the Advancement of Artificial Cognitive Sys- tems, Interaction and Robotics (ID: 2310, http://www.eucognition.org)

[AIinGames, 2008-2010] Member of An Industry/Academia Research Network on Artificial Intelligence and Games Technologies, EPSRC funded network (http://www.aigamesnetwork.org).

[CompuSteer, 2007-2008] Member of the CompuSteer computational steering network, EPSRC funded network (http://compusteer.net.dcs.hull.ac.uk).

[EURON II, 2004-2007] Represented DMU in EURON Network of Excellence (EU FP6) [EURON I, 2002-2004] Represented DMU in EURON Network of Excellence (EU FP5) [AgentLink I, 2001-2003] Represented DMU in AgentLink Network of Excellence (EU FP5)

Current research students

Current PhD Students
Names of Research Students	Mode	Supervisory Role
Alotaibi, Awad Gazzai	Full Time	1st supervisor
Mishael, Qadri Izzat Mohammad	Full Time	1st supervisor
Muhammad, Musa Abubakar	Full Time	1st supervisor
Wang, Ruijie	Full Time	2nd supervisor

Externally funded research grants information