Dr Francois Siewe

Job: Reader in Computer Science

Faculty: Technology

School/department: School of Computer Science and Informatics

Research group(s): Cyber Technology Institute (CTI) (Software Technology Research Laboratory (STRL))

Address: De Montfort University, The Gateway, Leicester, LE1 9BH, United Kingdom

T: +44 (0)116 257 7938

E: FSiewe@dmu.ac.uk

W: www.cse.dmu.ac.uk/~fsiewe/

 

Personal profile

I am a Reader in Computer Science in the Software Technology Research Laboratory (STRL) of the Faculty of Technology at De Montfort University (DMU), in Leicester in the UK. Before joining STRL, I was a research fellow on the EPSRC-funded project MELANGE on modelling the structure dependent colour properties of melange yarns in the Textile Engineering And Material (TEAM) research group at DMU. This followed my tenure as lecturer and vistiting researcher in the Institute of Technology of Lens at University of Artois in Lens, France. Prior to this, I was a fellow at the United Nation University/ International Institute for Software Technology (UNU/IIST) in Macau, where I worked on the Design Techniques for Reat-time systems (DeTfoRs) project. I was also a lecturer in the Department of Mathematics and Computer Science at the University of Dschang, Dschang, Cameroon.

Research group affiliations

Software Technology Research Laboratory (STRL)

Publications and outputs 

  • A Novel Algorithm for Course Learning Object Recommendation Based on Student Learning Styles
    A Novel Algorithm for Course Learning Object Recommendation Based on Student Learning Styles Siewe, Francois; He, Ying; Nafea, Shaimaa Explosive growth of e-learning in the recent years has faced difficulty of locating appropriate learning resources to match the students learning styles. Recommender system is a promising technology in e-learning environments to present personalised offers and convey appropriate learning objects that match student inclinations. This paper, proposes a novel and effective recommender algorithm that recommends personalised learning objects based on the student learning styles. Various similarity metrics are considered in an experimental study to investigate the best similarity metrics to use in a recommender system for learning objects. The approach is based on the Felder and Silverman learning style model which is used to represent both the student learning styles and the learning object profiles. It was found that the K-means clustering algorithm, the cosine similarity metrics and the Pearson correlation coefficient are effective tools for implementing learning object recommender systems. The accuracy of the recommendations are measured using traditional evaluation metrics, namely the Mean Absolute Error and the Root Mean Squared Error.
  • ULEARN: Personalized Course Learning Objects Based on Hybrid Recommendation Approach
    ULEARN: Personalized Course Learning Objects Based on Hybrid Recommendation Approach Nafea, Shaimaa; Siewe, Francois; He, Ying The success of e-learning systems depends on their capability to automatically retrieve and recommend relevant learning content according to the preferences of specific learner profiles. Generally, e-learning systems do not cater for individual learners’ needs based on their profile. They also make it very difficult for learners to choose suitable resources for their learning. Matching the teaching strategy with the most appropriate learning object based on learning styles is presented in this paper, with the aim of improving learners’ academic levels. This work focuses on the design of a personalized e-learning environment based on a hybrid recommender system, collaborative filtering and item content filtering. It also describes the architecture of the ULEARN system. The ULEARN uses a recommender adaptive teaching strategy by choosing and sequencing learning objects that fit with the learners’ learning styles. The proposed system can be used to rearrange learning object priority to match the student’s adaptive profile and to adapt teaching strategy, in order to improve the quality of learning. 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.
  • Formal Specification of a Context-aware Whiteboard System in CCA
    Formal Specification of a Context-aware Whiteboard System in CCA Atbaiga, N.; Siewe, Francois A context-aware whiteboard system provides a number of services in a smart classroom including registering students as they enter the classroom; logging students and lecturers in to the blackboard virtual learning environment at the beginning of each lecture and logging them out at the end of the lecture. This system also notifies students of their absence to a lecture and maintains a list of attendance automatically. Using information from the timetable, it is aware of the lectures that are scheduled to take place in the classroom and the students that are allowed to attend these lectures. Finally, it allows students and lecturers to interact with teaching materials such as lecture slides and videos stored in the blackboard virtual learning environment. This paper proposes a formal specification of the white board system in the Calculus of Context-aware Ambients (CCA in short). This enables the formal analysis of the white board system using the execution environment of CCA. Some important properties of a classroom white board system have been validated as a proof of concept.
  • ULEARN: Personalized course Learning Objects based on Hybrid Recommendation approach
    ULEARN: Personalized course Learning Objects based on Hybrid Recommendation approach Nafea, Shaimaa; Siewe, Francois; He, Ying Adaptive e-learning recommender system is observed one of the exciting research discipline in the education and teaching throughout the past few decades, since, the learning style is specific for each student In reality from the knowledge of his/ her learning style; matching teaching strategy with the most appropriate learning object is present to better return on learner academic level. This work focuses on the design of a personalized e-learning environment based on hybrid recommender system based on collaborative filtering and item content filtering as well as architecture of ULEARN system. ULEARN recommended adaptive teaching strategy by choosing and sequencing learning objects fitting with the learners’ learning styles. The proposed system can be used to rearrange learning object priority that matches student adaptive profile and teaching strategy in order to improve the quality of learning.
  • ULEARN: Personalised Learner’s Profile Based On Dynamic Learning Style Questionnaire
    ULEARN: Personalised Learner’s Profile Based On Dynamic Learning Style Questionnaire Nafea, Shaimaa; Siewe, Francois; He, Ying E-Learning recommender system effectiveness re- lies upon their ability to recommend appropriate learning con- tents according to the learner learning style and preferences. An effective approach to handle the learner preferences is to build an efficient learner profile in order to gain adaptation and individualisation of the learning environment. It is usually necessary to know learning style and preferences of the learner on a domain before adapting the learning process and course content. This study focuses on identifying the learning styles of students in order to adapt the learning process and course content. ULEARN is an adaptive recommender learning system designed to provide learners with personalised learning environment such as course learning objects that match their adaptive profile. This paper presents the algorithm used in ULEARN to reduce dynamically the number of questions in Felder-Silverman learning style ques- tionnaire used to initialise the adaptive learner profile. Firstly, the questionnaire is restructured into four groups, one for each learning style dimension; and a study is carried out to determine the order in which questions will be asked in each dimension. Then an algorithm is built upon this ranking of questions to calculate dynamically the initial learning style of the user as they go through the questionnaire. The file attached to this record is the author's final peer reviewed version.
  • A Multi-Layer Framework for Quality of Context in Ubiquitous Context-Aware Systems
    A Multi-Layer Framework for Quality of Context in Ubiquitous Context-Aware Systems Al-Shargabi, A. A.; Siewe, Francois This paper proposes a novel framework for Quality of Context (QoC) in context-aware systems. The main innovative features include: (1) a new definition that generalizes the notion of QoC; (2) a novel multilayer context model; (3) a novel model of QoC that introduces new quality parameters; (4) a novel mechanism to define QoC policy by assigning weights to QoC parameters using a multi-criteria decision-making technique; (5) and a novel quality control algorithm that handles context conflicts, context missing values, and context erroneous values. Our frameworkis implemented in MatLab and evaluated using a case study of a flood forecast system. 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.
  • An Adaptive Learning Ontological Framework Based on Learning Styles and Teaching Strategies
    An Adaptive Learning Ontological Framework Based on Learning Styles and Teaching Strategies Nafea, Shaimaa; Siewe, Francois; He, Ying Ontology are increasingly being used in a variety of applications, and particularly in adaptive e-learning. They have the potential to enable developers to create adaptive course content for specified domains. E-learning applications are thus able to use technology and educational content in order to generate content that matches the student's capabilities and knowledge. This personalises learning, rather than assuming that "one-size-fits-all" and providing all learners with the same content, which is what the majority of e-learning systems do. This study introduces a new approach that takes into account the fact that each learner has an individual learning style and needs. The approach enables to adapt the course content, teaching strategy and learning objects so that they correspond to each student’s learning styles. This is achieved with the use of artificial intelligent in the form of an ontology and rule-based reasoning. The proposed system takes some of the key design aspects such as extensibility, reusability, and maintainability into consideration in order to enhance performance of adaptive course content recommendation.
  • Quality of Context in Context-Aware Systems
    Quality of Context in Context-Aware Systems Al-Shargabi, A. A.; Siewe, Francois; Zahary, A. Context-aware Systems (CASs) are becoming increasingly popular and can be found in the areas of wearable computing, mobile computing, robotics, adaptive and intelligent user interfaces. Sensors are the corner stone of context capturing however, sensed context data are commonly prone to imperfection due to the technical limitations of sensors, their availability, dysfunction, and highly dynamic nature of environment. Consequently, sensed context data might be imprecise, erroneous, conflicting, or simply missing. To limit the impact of context imperfection on the behavior of a context-aware system, a notion of Quality of Context (QoC) is used to measure quality of any information that is used as context information. Adaptation is performed only if the context data used in the decision-making has an appropriate quality level. This paper reports an analytical review for state of the art quality of context in context-aware systems and points to future research directions.
  • A Framework for Minimising Data Leakage from Non-Production Systems
    A Framework for Minimising Data Leakage from Non-Production Systems Cope, Jacqueline; Maglaras, Leandros; Siewe, Francois; Chen, Feng; Janicke, Helge There is much research and advice around de-identification techniques and data governance. This brings together the practical aspects to propose a simplified business model and framework for informed decision making for the minimisation of data leakage from non-production systems using the topology of data classification, data protection and the requirements of non-production environments. The simplified model details the influences of legal and regulatory and business requirements on business systems and non-production environments. The framework identifies six stages, and the interactions required to progress from the legal and regulatory standards applicable to political and geographical areas, through organisational requirements and business system to the purpose of the non-production environment to data treatment and protection, with a demonstration of compliance which occurs throughout each stage of the framework. A table top exercise following a hypothetical, but realistic, scenario validates the model and framework.
  • On Data Leakage from Non-production Systems
    On Data Leakage from Non-production Systems Cope, Jacqueline; Siewe, Francois; Chen, Feng; Maglaras, Leandros; Janicke, Helge This study is an exploration of areas pertaining to the use of production data in non-production environments. During the software development lifecycle, non-production environments are used to serve various purposes to include unit, component, integration, system, user acceptance, performance and configuration testing. Organisations and third parties have been and are continuing to use copies of production data in non-production environments. This can lead to personal and sensitive data being accidentally leaked if appropriate and rigorous security guidelines are not implemented. This paper proposes a comprehensive framework for minimising data leakage from non-production environments. The framework was evaluated using guided interviews and was proven effective in helping organisation managing sensitive data in non-production environments. 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.

Click here to view a full listing of Francois Siewe's publications and outputs.

Research interests/expertise

Computer Security

Pervasive Systems

Formal Methods

Process Calculi

Formal Verification

Qualifications

PhD in Computer Science, De Montfort University, Leicester, UK, 2005

Doctorat de Troisième Cycle, Université de Yaoundé I, Yaoundé, Cameroon, 1997

Msc in Computer Science, Université de Yaoundé I, Yaoundé, Cameroon, 1992

Bsc in Maths & Computer Science, Université de Yaoundé, Yaoundé, Cameroon, 1990

Courses taught

Pervasive Systems

Honours and awards

Research Journal of Textile and Apparel (RJTA) Excellent Paper Award 2011

Current research students

First supervisor for:

Ahmed Mohammed Alalshuhai
Abdulgader Zaid Almutairi
Saud Maqed Almutairi
Muslit Awadh Alotaibi
Asma Abdulghani Qasem Al-Shargabi
Elena Chernikova
Abdulghani Mahmoud Suwan

Second supervisor for:

Abdullah Shawan Alotaibi
Amr Mohsen Jadi

Francios-Siewe

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