HIERARCHICAL-GRANULARITY HOLONIC MODELLING

This thesis aims to introduce an agent-based system engineering approach, named Hierarchical-Granularity Holonic Modelling, to support intelligent information processing at multiple granularity levels. The focus is especially on complex hierarchical systems. Nowadays, due to ever growing complexity of information systems and processes, there is an increasing need of a simple self-modular computational model able to manage data and perform information granulation at different resolutions (i.e., both spatial and temporal). The current literature lacks to provide such a methodology. To cite a relevant example, the object-oriented paradigm is suitable for describing a system at a given representation level; notwithstanding, further design effort is needed if a more synthetical of more analytical view of the same system is required. In the literature, the agent paradigm represents a viable solution in complex systems modelling; in particular, Multi-Agent Systems have been applied with success in a countless variety of distributed intelligence settings. Current agent-oriented implementations however suffer from an apparent dichotomy between agents as intelligent entities and agents\u2019 structures as superimposed hierarchies of roles within a given organization. The agents\u2019 architectures are often rigid and require intense re-engineering when the underpinning ontology is updated to cast new design criteria. The latest stage in the evolution of modelling frameworks is represented by Holonic Systems, based on the notion of \u2018holon\u2019 and \u2018holarchy\u2019 (i.e., hierarchy of holons). A holon, just like an agent, is an intelligent entity able to interact with the environment and to take decisions to solve a specific problem. Contrarily to agent, holon has the noteworthy property of playing the role of a whole and a part at the same time. This reflects at the organizational level: holarchy functions first as autonomous wholes in supra-ordination to their parts, secondly as dependent parts in sub-ordination to controls on higher levels, and thirdly in coordination with their local environment. These ideas were originally devised by Arthur Koestler in 1967. Since then, Holonic Systems have gained more and more credit in various fields such as Biology, Ecology, Theory of Emergence and Intelligent Manufacturing. Notwithstanding, with respect to these disciplines, fewer works on Holonic Systems can be found in the general framework of Artificial and Computational Intelligence. Moreover, the distance between theoretic models and actual implementation is still wide open. In this thesis, starting from the Koestler\u2019s original idea, we devise a novel agent-inspired model that merges intelligence with the holonic structure at multiple hierarchical-granularity levels. This is made possible thanks to a rule-based knowledge recursive representation, which allows the holonic agent to carry out both operating and learning tasks in a hierarchy of granularity levels. The proposed model can be directly used in terms of hardware/software applications. This endows systems and software engineers with a modular and scalable approach when dealing with complex hierarchical systems. In order to support our claims, exemplar experiments of our proposal are shown and prospective implications are commented

Stable Feature Selection for Biomarker Discovery

Feature selection techniques have been used as the workhorse in biomarker discovery applications for a long time. Surprisingly, the stability of feature selection with respect to sampling variations has long been under-considered. It is only until recently that this issue has received more and more attention. In this article, we review existing stable feature selection methods for biomarker discovery using a generic hierarchal framework. We have two objectives: (1) providing an overview on this new yet fast growing topic for a convenient reference; (2) categorizing existing methods under an expandable framework for future research and development

Abstract intelligence: Embodying and enabling cognitive systems by mathematical engineering

Basic studies in denotational mathematics and mathematical engineering have led to the theory of abstract intelligence (aI), which is a set of mathematical models of natural and computational intelligence in cognitive informatics (CI) and cognitive computing (CC). Abstract intelligence triggers the recent breakthroughs in cognitive systems such as cognitive computers, cognitive robots, cognitive neural networks, and cognitive learning. This paper reports a set of position statements presented in the plenary panel (Part II) of IEEE ICCI*CC’16 on Cognitive Informatics and Cognitive Computing at Stanford University. The summary is contributed by invited panelists who are part of the world’s renowned scholars in the transdisciplinary field of CI and CC

A finder and representation system for knowledge carriers based on granular computing

In one of his publications Aristotle states ”All human beings by their nature desire to know” [Kraut 1991]. This desire is initiated the day we are born and accompanies us for the rest of our life. While at a young age our parents serve as one of the principle sources for knowledge, this changes over the course of time. Technological advances and particularly the introduction of the Internet, have given us new possibilities to share and access knowledge from almost anywhere at any given time. Being able to access and share large collections of written down knowledge is only one part of the equation. Just as important is the internalization of it, which in many cases can prove to be difficult to accomplish. Hence, being able to request assistance from someone who holds the necessary knowledge is of great importance, as it can positively stimulate the internalization procedure. However, digitalization does not only provide a larger pool of knowledge sources to choose from but also more people that can be potentially activated, in a bid to receive personalized assistance with a given problem statement or question. While this is beneficial, it imposes the issue that it is hard to keep track of who knows what. For this task so-called Expert Finder Systems have been introduced, which are designed to identify and suggest the most suited candidates to provide assistance. Throughout this Ph.D. thesis a novel type of Expert Finder System will be introduced that is capable of capturing the knowledge users within a community hold, from explicit and implicit data sources. This is accomplished with the use of granular computing, natural language processing and a set of metrics that have been introduced to measure and compare the suitability of candidates. Furthermore, are the knowledge requirements of a problem statement or question being assessed, in order to ensure that only the most suited candidates are being recommended to provide assistance

Proceedings of the 1st Doctoral Consortium at the European Conference on Artificial Intelligence (DC-ECAI 2020)

1st Doctoral Consortium at the European Conference on Artificial Intelligence (DC-ECAI 2020), 29-30 August, 2020 Santiago de Compostela, SpainThe DC-ECAI 2020 provides a unique opportunity for PhD students, who are close to finishing their doctorate research, to interact with experienced researchers in the field. Senior members of the community are assigned as mentors for each group of students based on the student’s research or similarity of research interests. The DC-ECAI 2020, which is held virtually this year, allows students from all over the world to present their research and discuss their ongoing research and career plans with their mentor, to do networking with other participants, and to receive training and mentoring about career planning and career option

Facilitating file retrieval on resource limited devices

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The rapid development of mobile technologies has facilitated users to generate and store files on mobile devices. However, it has become a challenging issue for users to search efficiently and effectively for files of interest in a mobile environment that involves a large number of mobile nodes. In this thesis, file management and retrieval alternatives have been investigated to propose a feasible framework that can be employed on resource-limited devices without altering their operating systems. The file annotation and retrieval framework (FARM) proposed in the thesis automatically annotates the files with their basic file attributes by extracting them from the underlying operating system of the device. The framework is implemented in the JME platform as a case study. This framework provides a variety of features for managing the metadata and file search features on the device itself and on other devices in a networked environment. FARM not only automates the file-search process but also provides accurate results as demonstrated by the experimental analysis. In order to facilitate a file search and take advantage of the Semantic Web Technologies, the SemFARM framework is proposed which utilizes the knowledge of a generic ontology. The generic ontology defines the most common keywords that can be used as the metadata of stored files. This provides semantic-based file search capabilities on low-end devices where the search keywords are enriched with additional knowledge extracted from the defined ontology. The existing frameworks annotate image files only, while SemFARM can be used to annotate all types of files. Semantic heterogeneity is a challenging issue and necessitates extensive research to accomplish the aim of a semantic web. For this reason, significant research efforts have been made in recent years by proposing an enormous number of ontology alignment systems to deal with ontology heterogeneities. In the process of aligning different ontologies, it is essential to encompass their semantic, structural or any system-specific measures in mapping decisions to produce more accurate alignments. The proposed solution, in this thesis, for ontology alignment presents a structural matcher, which computes the similarity between the super-classes, sub-classes and properties of two entities from different ontologies that require aligning. The proposed alignment system (OARS) uses Rough Sets to aggregate the results obtained from various matchers in order to deal with uncertainties during the mapping process of entities. The OARS uses a combinational approach by using a string-based and linguistic-based matcher, in addition to structural-matcher for computing the overall similarity between two entities. The performance of the OARS is evaluated in comparison with existing state of the art alignment systems in terms of precision and recall. The performance tests are performed by using benchmark ontologies and the results show significant improvements, specifically in terms of recall on all groups of test ontologies. There is no such existing framework, which can use alignments for file search on mobile devices. The ontology alignment paradigm is integrated in the SemFARM to further enhance the file search features of the framework as it utilises the knowledge of more than one ontology in order to perform a search query. The experimental evaluations show that it performs better in terms of precision and recall where more than one ontology is available when searching for a required file.Education Commission of Pakistan and the University of Engineering & Technology, Peshawa

The management of burn wounds by nurses

A thesis submitted to the Faculty of Health Sciences, University of the Witwatersrand, in fulfilment of the requirements for the degree of Doctor of Philosophy Johannesburg, 2015A standardised approach to wound care is vital if a positive outcome is expected. The positive outcomes of standardisation and evidence based wound care protocols have been well documented, yet nurses in South Africa do not have a standard that informs burn wound management. The purpose of this study is to describe the best available evidence for management of burn wounds and to explore nurses’ current practices in a single burns unit with the aim of developing guidelines to inform nursing practices. A QUAN (quantitative dominant) QUAN+ QUAL (quantitative and quantitative concurrently), a non- experimental explanatory sequential descriptive design was used. The process was divided into three phases: Phase One involved the search for quality evidence through an integrative review. The main review question was: “What new knowledge or information related to non-surgical management of burn wounds has emerged in the literature between 2000 and 2014?” Eleven sub questions were used to guide the literature search according to the themes of the nursing process of: Assessment, Diagnosis, Intervention, Outcome and Evaluation. The review process included a problem identification stage, literature search stage, data evaluation stage, data analysis stage and presentation stage. The included literature was based on a hierarchy of evidence. The search strategy included: multiple electronic databases, hand searching, reference lists of relevant articles, comments of experts, textbook chapters compiled by experts and guidelines. The final sample consisted of n= 354 studies. A qualitative descriptive approach was used to synthesise the research findings. Phase Two involved the study of current practice through structured observation and semi-structured interviews. The purpose of Phase Two was to obtain first-hand information in a naturally occurring situation to identify the strengths, weaknesses and gaps in current practices. Purposive sampling was undertaken and included all nurses providing care to patients with superficial to partial thickness burn wounds. A total of n= 303 dressings were observed and eight interviews were conducted. Phase Three was the verification of findings from Phases One and Two by experts in the field using the AGREE II instrument. Conclusions drawn from observations and interviews were integrated and synthesised with the conclusions from the integrative review. These conclusions were used to develop guidelines for the management of burn wounds by nurses

Development and characterisation of MSC-seeded decellularised airway scaffolds for regenerative bioengineering

Tracheal tissue engineering (TE) is a potential solution for long tracheal lesions and recent clinical experience yielded promising results but challenges remain with respect to measurable criteria for acceptance of decellularised scaffolds, optimisation of cell seeding and understanding the biology of the seeded cells post attachment. Confirming previous data from our group, I showed cellular clearance of DC scaffolds and significant reduction in total DNA levels but observed retention of residual nuclear materials within hyaline cartilage and submucosa. Evaluation of extracellular matrix components demonstrated retention of collagen and glycosaminoglycan and disrupted basement membrane components. The novel use of dynamic mechanical analysis (DMA) to measure the viscoelastic properties of tracheal cartilage in addition to tensile testing, provided the first demonstration of preservation of native viscoelastic mechanical properties after decellularisation. To overcome the limitations of passive cell seeding, I conceived partial surface dehydration (PSD) conditioning of scaffolds which significantly improved cell seeding/attachment efficiency to (96.46% 1.710) and I confirmed survival of MSCs on the scaffold in vitro. Multiphoton imaging showed limited scaffold infiltration but revealed two, distinct cell morphologies dependent on the presence or absence of adventitia. These showed different RNA transcriptomic profiles and differential gene expression. Seeded MSCs upregulated transcripts of bioactive paracrine factors associated with tissue repair, including ECM remodelling, pro-angiogenesis, antifibrosis, chemoattraction and immunomodulatory properties. Cells seeded into the adventitial layer upregulated more bioactive factors and showed lower cellular stress, suggesting a favourable effect of maintaining adventitial layer. The data presented herein form a coherent series of experiments providing novel data to the field of tracheal tissue engineering which address important GMP issues such as in-process acceptance criteria for scaffolds and data to support the rationale of autologous MSC seeding prior to implantation. These results allowed us to manufacture an improved clinical product for a compassionate case