A High-Level Scheme for an Ontology-Based Compliance Framework in Software Development

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.Software development market is currently witnessing an increasing demand for software applications conformance with the international regime of GRC for Governance, Risk and Compliance. In this paper, we propose a compliance requirement analysis method for early stages of software development based on a semantically-rich model, where a mapping can be established from legal and regulatory requirements relevant to system context to software system business goals and contexts. The proposed semantic model consists of a number of ontologies each corresponding to a knowledge component within the developed framework of our approach. Each ontology is a thesaurus of concepts in the compliance and risk assessment domain related to system development along with relationships and rules between concepts that compromise the domain knowledge. The main contribution of the work presented in this paper is a case study that demonstrates how description-logic reasoning techniques can be used to simulate legal reasoning requirements employed by legal professions against the description of each ontology

An adaptive mobile learning system for learning a new language based on learner’s abilities

The rapid development of wireless infrastructure and wide use of mobile devices in our daily life has a major impact on our way of learning using computing technology. Particularly, learning a new language is a challenging concept for researcher. Furthermore, adaptive services is nowadays an important research topic in the field of web-based and mobile learning systems as there are no fixed learning path which are appropriate for all learners. However, most studies in this field have only focus on learning style and habits of learners. Far too little attention has been paid on the ability of learner. Therefore, the purpose of this paper is to propose a new adaptive mobile learning model for learning new languages based on ability of learner. Furthermore, an ontology-based knowledge modelling technique is proposed to classify language learning materials and describe user profile in order to provide adaptive learning environment

Adaptive e-learning system using ontology

This paper proposes an innovative ontological approach to design a personalised e-learning system which creates a tailored workflow for individual learner. Moreover, the learning content and sequencing logic is separated into content model and pedagogical model to increase the reusability and flexibility of the system

Ontologies for Personalised Adaptive Learning

In recent years there has been an increasing interest in individual education. Consequently, one of the hot research topics is to adapt learning content to learner’s learning needs. Furthermore, recent developments in the field of semantic web have led to a renewed attention with focus in ontology-based e-learning system. This paper proposes an innovative ontological approach to design a personalised e-learning system which creates tailored contents for individual learners. The learning content associated with sequencing logic provides a clear separation between the domain and content models to increase the reusability and flexibility of the system. Additionally, in the proposed approach learner’s profiles are modelled to describe learner’s characteristics

Personalised mobile learning system based on item response theory

Rapid advancements in the design and integration of mobile devices and networked technologies in day to day activities are creating new perceptions about the exploitation of mobile technologies in teaching and learning. Consequently, there is growing demand for personalised, efficient and flexible systems for supporting learning in various settings. However, fulfilling learner demand for personalised support requires better understanding of activities, operational contexts and purposes for which mobile devices are deployed to support learning. Therefore, our position with regards to methods for researching mobile learning focuses on personalised learning. This paper presents an approach to designing a personalised learning system by analysing the ability of the learner based on Items Response Theory. Furthermore, in the proposed system user profile is modelled based on profile ontology

Neurosymbolic Spike Concept Learner towards Neuromorphic General Intelligence

Current research in the area of concept learning makes use of deep learning and ensembles methods to learn concepts. Concept learning allows us to combine heterogeneous entities in data which could collectively identify as individual concepts. Heterogeneity and compositionality are crucial areas to explore in machine learning as it has the potential to contribute profoundly to artificial general intelligence. We investigate the use of spiking neural networks for concept learning. Spiking neurones inclusively model the temporal properties as observed in biological neurones. A benefit of spike-based neurones allows for localised learning rules that only adapts connections between relevant neurones. In this position paper, we propose a technique allowing dynamic formation of synapse (connections) in spiking neural networks, the basis of structural plasticity. Achieving dynamic formation of synapse allows for a unique approach to concept learning with a malleable neural structure. We call this technique Neurosymbolic Spike-Concept Learner (NS-SCL). The limitations of NS-SCL can be overcome with the neuromorphic computing paradigm. Furthermore, introducing NS-SCL as a technique on neuromorphic platforms should motivate a new direction of research towards Neuromorphic General Intelligence (NGI), a term we define to some extent

Towards an Ontology-Based Approach to Measuring Productivity for Offsite Manufacturing Method

The steady decline of manual and skilled trades in the construction industry has increased the recognition of offsite manufacturing (OSM), an aspect of Design for Manufacture and Assembly (DFMA) methods as one way to boost productivity and performance. However, existing productivity estimation approaches are carried out in isolation thus limiting the sort of result obtained from such systems. Also, there is yet to be a holistic approach that enables productivity estimation using different metrics and integrates experts’ knowledge to predict productivity and guide decision making at the early development stage of a project. This study aims to develop a method that can be used to generate multiple estimations for all these metrics simultaneously through linking their relationships. An ontology-based knowledge modelling approach for estimating productivity at the production stage for OSM projects is proposed. A case study of panel system offsite is used as a proof-of-concept for data collection and knowledge modelling in an ontology. Results from the study through the use of rules and semantic reasoning retrieved cost estimates and time schedule for a panel system production with considerations for different design choices. It is thus proven that systemising the production process knowledge of OSM methods enables practitioners to make informed choices on product design to best suit productivity requirements. The developed method helps to reduce the level of uncertainty by encouraging measurable evidence and allows for better decision-making on productivity

Demystifying the concept of offsite manufacturing method: Towards a robust definition and classification system

Purpose This study aims to develop a more inclusive working definition and a formalised classification system for offsite construction to enable common basis of evaluation and communication. Offsite manufacturing (OSM) is continuously getting recognised as a way to increase efficiency and boost productivity of the construction industry in many countries. However, the knowledge of OSM varies across different countries, construction practices and individual experts thus resulting into major misconceptions. The lack of consensus of what OSM is and what constitutes its methods creates a lot of misunderstanding across Architecture Engineering and Construction (AEC) industry professionals, therefore, inhibiting a global view and understanding for multicultural collaboration. Therefore, there is a need to revisit these issues with the aim to develop a deep understanding of the concepts and ascertain what is deemed inclusive or exclusive. Design/methodology/approach A state-of-the-art review and analysis of literature on OSM was conducted to observe trends in OSM definitions and classifications. The paper identifies gaps in existing methods and proposes a future direction. Findings Findings suggest that classifications are mostly aimed towards a particular purpose and existing classification system are not robust enough to cover all aspects. Therefore, there is need to extend these classification systems to be fit for various purposes. Originality/value This paper contributes to the body of literature on offsite concepts, definition and classification, and provides knowledge on the broader context on the fundamentals of OSM

Activities of daily life recognition using process representation modelling to support intention analysis

Purpose – This paper aims to focus on applying a range of traditional classification- and semantic reasoning-based techniques to recognise activities of daily life (ADLs). ADL recognition plays an important role in tracking functional decline among elderly people who suffer from Alzheimer’s disease. Accurate recognition enables smart environments to support and assist the elderly to lead an independent life for as long as possible. However, the ability to represent the complex structure of an ADL in a flexible manner remains a challenge. Design/methodology/approach – This paper presents an ADL recognition approach, which uses a hierarchical structure for the representation and modelling of the activities, its associated tasks and their relationships. This study describes an approach in constructing ADLs based on a task-specific and intention-oriented plan representation language called Asbru. The proposed method is particularly flexible and adaptable for caregivers to be able to model daily schedules for Alzheimer’s patients. Findings – A proof of concept prototype evaluation has been conducted for the validation of the proposed ADL recognition engine, which has comparable recognition results with existing ADL recognition approaches. Originality/value – The work presented in this paper is novel, as the developed ADL recognition approach takes into account all relationships and dependencies within the modelled ADLs. This is very useful when conducting activity recognition with very limited features

ARP cache poisoning mitigation and forensics investigation

Address Resolution Protocol (ARP) cache spoofing or poisoning is an OSI layer 2 attack that exploits the statelessness vulnerability of the protocol to make network hosts susceptible to issues such as Man in the Middle attack, host impersonation, Denial of Service (DoS) and session hijacking. In this paper, a quantitative research approach is used to propose forensic tools for capturing evidences and mitigating ARP cache poisoning. The baseline approach is adopted to validate the proposed tools. The evidences captured before attack are compared against evidences captured when the network is under attack in order to ascertain the validity of the proposed tools in capturing ARP cache spoofing evidences. To mitigate the ARP poisoning attack, the security features DHCP Snooping and Dynamic ARP Inspection (DAI) are enabled and configured on a Cisco switch. The experimentation results showed the effectiveness of the proposed mitigation technique