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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: http://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 Lightweight Association Rules Based Prediction Algorithm (LWRCCAR ) for Context-Aware Systems in IoT Ubiquitous, Fog, and Edge Computing Environment
    A Lightweight Association Rules Based Prediction Algorithm (LWRCCAR ) for Context-Aware Systems in IoT Ubiquitous, Fog, and Edge Computing Environment Al-Shargabi, Asma; Siewe, Francois Abstract- Proactive is one main aspect of ubiquitous context-aware systems in IoT environment. The power of artificial intelligent is employed to realize this high-end aspect. Ubiquitous context-aware systems in IoT environment needs a light-weight intelligent prediction techniques especially within fog and edge computing environment where technologies capabilities are poor. On the other hand, in big data area the amount of data used to train ubiquitous context-aware systems is huge. This paper suggests a light-weight prediction algorithm to help such systems to work more effectively. The proposed algorithm is an improvement of the RCCAR algorithm that we developed in previous work. RCCAR utilizes association rules for prediction. The contribution of this paper is to minimize the number of association rules by giving a priority to associations that produced of high order. The prediction is scored and formulated mathematically using the confidence measure of association rules. A real-world dataset is used to evaluate the proposed algorithm in various scenarios. The results show that the proposed algorithm achieves better prediction scores. For future work, extensive experiments with many datasets is recommended.
  • Performance of Execution Tracing with Aspect-Oriented and Conventional Approaches
    Performance of Execution Tracing with Aspect-Oriented and Conventional Approaches Galli, Tamas; Chiclana, Francisco; Siewe, Francois Context: Most software product quality models contain a quality property to describe the performance of the software products under assessment. In addition, software product quality models exhibit a quality property to describe how maintainable the investigated software product is. These two quality properties are conflicting as good maintainability values require that the amount of information about the threads of execution in the application, method stacks including parameters, return values and changes of the internal states be available. Collecting and storing this amount of information in a trace output takes time and consumes resources, which deteriorates execution performance. The major expectation towards the trace data is accuracy and consistency and, as a second priority, to achieve an acceptable performance degradation. Aspect-oriented programming offers assistance to satisfy these goals, however the published data about the aspect-oriented performance are not up to-date; moreover, they do not measure the performance of execution tracing but the performance of aspect-oriented programming constructs. Objectives: We aim to measure the performance of execution tracing with a simple but very resource intensive test application pair implementing the same functionality, producing the same amount of trace data. The one application possesses aspect-oriented execution tracing while the other one conventional, object-oriented execution-tracing with manually inserted trace method calls. In addition, we plan to separately measure the performance impact of constructing the trace messages by collecting the data in the applications; moreover, the performance impact of writing these data into a physical file is also measured. Method: We introduce a true experimental research with full control over the research variables to measure the runtimes of two very resource intensive test applications with exactly the same functionality, one with conventional tracing where the trace method calls were manually inserted at the entry and exit points of each method and the other one where the trace method calls were inserted by aspect-oriented programming with compile-time weaving automatically. We have selected such a pressing tracing policy to study the differences at maximal margin. In addition, no logging frameworks were used to rule out their performance effects. Both test applications produce the same bytes of trace data between the points of time measurements. Moreover, the two test applications were run in parallel, in three ways: (1) with deactivated tracing, (2) with activated tracing with output in /dev/null, (3) with activated tracing with output in a real file, to eliminate the impact of extraneous variables while computing runtime ratios of conventional and aspect-oriented execution tracing. As platform we have chosen Java and AspectJ in version 1.8 and 1.9, which at the time of writing the manuscript is the latest available AspectJ version, to examine whether Java and aspectJ versions have an impact on the performance of execution tracing. Results: The measurements produced results with very strong statistical significance (p < 0.001): (1) different Java and AspectJ versions have different performance impacts on execution tracing, (2) constructing the trace messages in the two test applications had more impact on the performance than writing these data in real files, (3) aspect-oriented implementation of execution tracing deteriorated the performance with deactivated tracing compared to the non-aspect-oriented implementation, (4) the aspect-oriented implementation of execution tracing produced only an acceptable performance overhead with activated tracing compared to the non-aspect-oriented counterpart with activated tracing. Measurement data are provided in the appendix.
  • Analysing Petri Nets in a Calculus of Context-aware Ambients
    Analysing Petri Nets in a Calculus of Context-aware Ambients Siewe, Francois; Germanos, Vasileios; Zeng, W. This paper proposes an approach to analysing and verifying Petri nets using a Calculus of Context-aware Ambients (CCA). We propose an algorithm that transforms a Petri net into a CCA process. This demonstrates that any system that can be specified in Petri nets can also be specified in CCA. Besides, the system can be analysed and verified using the CCA verification tools. We illustrate the practicality of our approach using a case study of the dining cryptographers problem.
  • How Location-Aware Access Control Affects User Privacy and Security in Cloud Computing Systems
    How Location-Aware Access Control Affects User Privacy and Security in Cloud Computing Systems Zeng, W.; Bashir, Reem; Wood, Trevor; Siewe, Francois; Janicke, Helge; Wagner, Isabel The use of cloud computing (CC) is rapidly increasing due to the demand for internet services and communications. The large number of services and data stored in the cloud creates security risks due to the dynamic movement of data, connected devices and users between various cloud environments. In this study, we will develop an innovative prototype for location-aware access control and data privacy for CC systems. We will apply location-aware access control policies to role-based access control of Cloud Foundry, and then analyze the impact on user privacy after implementing these policies. This innovation can be used to address the security risks introduced by inter-cloud use and communication, and will have significant impact in making citizen’s personal data more secure. open access article
  • Software Product Quality Models, Developments, Trends and Evaluation
    Software Product Quality Models, Developments, Trends and Evaluation Galli, Tamas; Chiclana, Francisco; Siewe, Francois Software product quality models have evolved in their abilities to capture and describe the abstract notion of software quality since the 1970’s. Many models constructed deal with a specific part of software quality only which makes them ineligible to assess the quality of software products as a whole. Former publications failed to thoroughly examine and list all the available models which attempt to describe each known property of software product quality. This paper discovers such complete software product quality models published since 2000; moreover, it endeavours to measure the relevance of each model quantitatively by introducing indicators with regard to the scientific and industrial community. The identified 23 software product quality model classes differ significantly in terms of publication intensity, publication range, quality score average, relevance score and the 12-month average of the Google Relative Search Index. The results offer a foundation for selecting the appropriate software product quality model for use or for extension if newly identified quality properties need to be connected to a general context. Furthermore, the experiences accumulated on the field of software product quality modelling motivated researchers to successfully transfer the concepts to other areas where abstract entities need to be compared or assessed including the quality of higher educational teaching and business processes, which is also briefly highlighted in the paper. 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.
  • Agent-based Negotiation Approach for Feature Interactions in Smart Home Systems using Calculus of the Context-aware Ambient
    Agent-based Negotiation Approach for Feature Interactions in Smart Home Systems using Calculus of the Context-aware Ambient Alfakeeh, Ahmed S.; Al-Bayatti, Ali H.; Siewe, Francois; Baker, Thar Smart Home Systems (SHSs) provide several services which are tailored to different residents’ preferences. As a result, SHSs are highly exposed to undesirable interactions, known as feature interactions (FIs). FIs might occur as a result of a conflict in services’ goals or a conflict with residents’ preferences. Previous studies have proposed solutions based on applying priorities, in which some services or preferable features are disabled in favour of other services. Alternatively, the agent-based negotiation approach (ABNA) utilises agents and applies negotiation, enabling services with contrary features to work simultaneously. ABNA avoids applying priority between services or house residents’ preferences whenever a space for a compromise exists. The mechanism of ABNA is based on the use of a hierarchy of features based on their contribution to the function of the service or on the importance of these features to house residents. To achieve a compromise between conflicting services, ABNA models services and residents by using agents, and implements a negotiation algorithm that allows services with conflicting features to work simultaneously. This paper presents a description of ABNA with a formal specification of ABNA in the Calculus of Context-aware Ambient (CCA). This enables the formal analysis of ABNA by using the execution environment of CCA. The file attached to this record is the author's final peer reviewed version.
  • A novel algorithm for dynamic student profile adaptation based on learning styles
    A novel algorithm for dynamic student profile adaptation based on learning styles Nafea, Shaimaa M.; Siewe, Francois; He, Ying E-learning recommendation systems are used to enhance student performance and knowledge by providing tailor- made services based on the students’ preferences and learning styles, which are typically stored in student profiles. For such systems to remain effective, the profiles need to be able to adapt and reflect the students’ changing behaviour. In this paper, we introduce new algorithms that are designed to track student learning behaviour patterns, capture their learning styles, and maintain dynamic student profiles within a recommendation system (RS). This paper also proposes a new method to extract features that characterise student behaviour to identify students’ learning styles with respect to the Felder-Silverman learning style model (FSLSM). In order to test the efficiency of the proposed algorithm, we present a series of experiments that use a dataset of real students to demonstrate how our proposed algorithm can effectively model a dynamic student profile and adapt to different student learning behaviour. The results revealed that the students could effectively increase their learning efficiency and quality for the courses when the learning styles are identified, and proper recommendations are made by using our method. 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.
  • On Recommendation of Learning Objects using Felder-Silverman Learning Style Model
    On Recommendation of Learning Objects using Felder-Silverman Learning Style Model Nafea, Shaimaa M.; Siewe, Francois; He, Ying The e-learning recommender system in learning institutions is increasingly becoming the preferred mode of delivery, as it enables learning anytime, anywhere. However, delivering personalised course learning objects based on learner preferences is still a challenge. Current mainstream recommendation algorithms, such as the Collaborative Filtering (CF) and Content-Based Filtering (CBF), deal with only two types of entities, namely users and items with their ratings. However, these methods do not pay attention to student preferences, such as learning styles, which are especially important for the accuracy of course learning objects prediction or recommendation. Moreover, several recommendation techniques experience cold-start and rating sparsity problems. To address the challenge of improving the quality of recommender systems, in this paper a novel recommender algorithm for machine learning is proposed, which combines students actual rating with their learning styles to recommend Top-N course learning objects (LOs). Various recommendation techniques are considered in an experimental study investigating the best technique to use in predicting student ratings for e-learning recommender systems. We use the Felder-Silverman Learning Styles Model (FSLSM) to represent both the student learning styles and the learning object profiles. The predicted rating has been compared with the actual student rating. This approach has been experimented on 80 students for an online course created in the MOODLE Learning Management System, while the evaluation of the experiments has been performed with the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The results of the experiment verify that the proposed approach provides a higher prediction rating and significantly increases the accuracy of the recommendation. 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.
  • 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.

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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