
George BaryannisUniversity of Huddersfield · Department of Computer Science
George Baryannis
PhD
About
57
Publications
30,546
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
1,576
Citations
Introduction
I am Subject Area Leader for Computing and Information Systems at the University of Huddersfield and the Deputy Director of the Centre for Autonomous and Intelligent Systems. I received a DiplEng in Electronic and Computer Engineering from the Technical University of Crete, Greece and a MSc and PhD in Computer Science from the University of Crete, Greece. I have over 14 years of experience in research within the broader area of Artificial Intelligence and applications.
Additional affiliations
May 2020 - present
April 2019 - April 2020
September 2016 - July 2017
Education
September 2009 - July 2014
February 2007 - June 2009
September 2001 - October 2006
Publications
Publications (57)
This paper presents a rule-based approach for both offline and real-time recognition of Activities of Daily Living (ADL), leveraging events produced by a non-intrusive multi-modal sensor infrastructure deployed in a residential environment. Novel aspects of the approach include: the ability to recognise arbitrary scenarios of complex activities usi...
We propose a composition and verification framework for Semantic Web Services specified using WSSL, a novel service specification language based on the fluent calculus, that addresses issues related to the frame, ramification and qualification problems. These deal with the succinct and flexible representation of non-effects, indirect effects and pr...
In order to effectively discover and invoke a Web service, the provider must supply a complete specification of its behavior, with regard to its inputs, outputs, preconditions and effects. Devising such complete specifications comes with many issues that have not been adequately addressed by current service description efforts, such as WSDL, SAWSDL...
In this paper we identify current challenges in the deployment of complex distributed applications on multiple Cloud providers and review the state of the art in model-driven Cloud software engineering. Challenges include lack of support for heterogeneous Cloud providers; limited matchmaking between application requirements and Cloud capabilities;...
In the S-Cube research framework, the Service Composition and Co-ordination (SCC) layer encompasses the functions required for the aggregation of multiple services into a single composite service offering, with the execution of the constituent services in a composition controlled through the Service Infrastructure (SI) layer. The SCC layer manages...
The ultimate goal for developing machine learning models in supply chain management is to make optimal interventions. However, most machine learning models identify correlations in data rather than inferring causation, making it difficult to systematically plan for better outcomes. In this article, we propose and evaluate the use of causal machine...
The global halal market is growing, driven by rising stakeholder populations and increasing consumer interest in ethical and sustainable food choices. This surge in demand necessitates robust halal compliance throughout complex supply chains. However, there are several challenges, including fragmented information, increased understanding of halal r...
The penultimate goal for developing machine learning models in supply chain management is to make optimal interventions. However, most machine learning models identify correlations in data rather than inferring causation, making it difficult to systematically plan for better outcomes. In this article, we propose and evaluate the use of causal machi...
Supplier selection has become increasingly complex regarding selection criteria caused by expanded data collection processes and supplier numbers due to globalisation effects. This complexity has led to the consideration of Artificial Intelligence (AI) techniques to facilitate and enhance supplier selection. However, the AI techniques most often ap...
The need for supply chains to be resilient is increasingly being recognised, following recent disruptions caused by global socioeconomic crises. Supply chain resilience allows for sustainable growth and development through adaptive capabilities, principally including the ability to effectively respond to disruptions to maintain consistent operation...
Background: This study addresses challenges faced by supply chain stakeholders who lack expert knowledge in making decisions related to Machine Learning. It introduces a novel use of Multi-Criteria Decision-Making as an evaluation mechanism for different classifiers, aiding stakeholders in selecting appropriate Machine Learning models for predictin...
Despite a profusion of literature on Complex Adaptive System (CAS) definitions, it is still challenging to definitely answer whether a given system is or is not a CAS. The challenge generally lies in deciding where boundaries lie between a Complex System (CS) and a CAS. In this work, we propose a novel definition for CAS in the form of a concise, r...
Purpose
Attention Deficit Hyperactivity Disorder (ADHD) is a widespread condition that affects human behaviour and can interfere with daily activities and relationships. Medication or medical information about ADHD can be found in several data sources on the Web. Such distribution of knowledge raises notable obstacles since researchers and clinicia...
Automated planning is a prominent area of Artificial Intelligence and an important component for intelligent autonomous agents. A cornerstone of domain-independent planning is the separation between planning logic, that is the automated reasoning side, and the knowledge model, that encodes a formal representation of domain knowledge needed to reaso...
Artificial Intelligence (AI) has emerged as a complementary technology in supply chain research. However, the majority of AI approaches explored in this context afford little to no explainability, which is a significant barrier to a broader adoption of AI in supply chains. In recent years, the need for explainability has been a strong impetus for r...
Evaluating and selecting suitable suppliers involves several criteria, making it a complex and difficult problem for decision-makers. Several approaches have been developed to help achieve the best possible results for selecting the most suitable suppliers. Traditionally, multi-criteria decision-making methods, such as the recently introduced MARCO...
While the increased use of AI in the manufacturing sector has been widely noted, there is little understanding on the risks that it may raise in a manufacturing organisation. Although various high level frameworks and definitions have been proposed to consolidate potential risks, practitioners struggle with understanding and implementing them. This...
Automated planning is a prominent area of Artificial Intelligence, and an important component for intelligent autonomous agents. A cornerstone of domain-independent planning is the separation between planning logic, i.e. the automated reasoning side, and the knowledge model, that encodes a formal representation of domain knowledge needed to reason...
The high precedence of epidemiological examination of skin lesions necessitated the well-performing efficient classification and segmentation models. In the past two decades, various algorithms, especially machine/deep learning-based methods, replicated the classical visual examination to accomplish the above-mentioned tasks. These automated stream...
Modal logic S5 is used extensively for representing knowledge that includes statements about necessity and possibility, owing to its simplicity in handling chained modal operators. Significant research effort has been devoted in developing efficient reasoning mechanisms over complex S5 formulas, resulting in various solvers taking advantage of the...
Knowledge representation and reasoning in the legal domain has primarily focused on case studies where knowledge and data can fit in main memory. However, this assumption no longer applies in the era of big data, where large amounts of data are generated daily. This paper discusses new opportunities and challenges that emerge in relation to reasoni...
Mental illnesses are becoming increasingly prevalent, in turn leading to an increased interest in exploring artificial intelligence (AI) solutions to facilitate and enhance healthcare processes ranging from diagnosis to monitoring and treatment. In contrast to application areas where black box systems may be acceptable, explainability in healthcare...
Modal logic S5 has attracted significant attention and has led to several practical applications, owing to its simplified approach to dealing with nesting modal operators. Efficient implementations for evaluating satisfiability of S5 formulas commonly rely on Skolemisation to convert them into propositional logic formulas, essentially by introducin...
Modal logic S5 has attracted significant attention and has led to several practical applications, owing to its simplified approach to dealing with nesting modal operators. Efficient implementations for evaluating satisfiability of S5 formulas commonly rely on Skolemisation to convert them into propositional logic formulas, essentially by introducin...
Traditionally, computational knowledge representation and reasoning focused its attention on rich domains such as the law. The main underlying assumption of traditional legal knowledge representation and reasoning is that knowledge and data are both available in main memory. However, in the era of big data, where large amounts of data are generated...
Neural networks have achieved in recent years human level performance in various application domains, including critical applications where accountability is a very important issue, closely related to the interpretability of neural networks and artificial intelligence in general. In this work, an approach for defining the structure of neural networ...
Qualitative reasoning involves expressing and deriving knowledge based on qualitative terms such as natural language expressions, rather than strict mathematical quantities. Well over 40 qualitative calculi have been proposed so far, mostly in the spatial and temporal domains, with several practical applications such as naval traffic monitoring, wa...
Qualitative reasoning involves expressing and deriving knowledge based on qualitative terms such as natural language expressions, rather than strict mathematical quantities. Well over 40 qualitative calculi have been proposed so far, mostly in the spatial and temporal domains, with several practical applications such as naval traffic monitoring, wa...
Neural networks have achieved in recent years human level performance in various application domains, including critical applications where accountability is a very important issue, closely related to the interpretability of neural networks and artificial intelligence in general. In this work, an approach for defining the structure of neural networ...
The process of converting natural language specifications into conceptual models requires detailed analysis of natural language text, and designers frequently make mistakes when undertaking this transformation manually. Although many approaches have been used to partly automate this process, one of the main limitations is the lack of a domain-indep...
Supplier selection is an important part of supply chain management (SCM) for any organisation to achieve their objectives. The problem has attracted great interest from academics and practitioners. The selection process starts with determining the most important criteria out of a wide range. Many academic researchers apply multi-criteria decision-m...
The problem of discovering regions that support particular functionalities in an urban setting has been approached in literature using two general methodologies: top-down, encoding expert knowledge on urban planning and design and discovering regions that conform to that knowledge; and bottom-up, using data to train machine learning models, which c...
Managing supply chain risks has received increased attention in recent years, aiming to shield supply chains from disruptions by predicting their occurrence and mitigating their adverse effects. At the same time, the resurgence of Artificial Intelligence (AI) has led to the investigation of machine learning techniques and their applicability in sup...
The problem of identifying functional regions in an urban setting has been approached in literature using two general methodologies: top-down, encoding expert knowledge on urban planning and design (e.g. into patterns) and using that knowledge for identification, and bottom-up, relying on crowdsourcing and Volunteered Geographic Information (VGI) t...
Searching for places rather than traditional keyword-based search represents significant challenges. The most prevalent method of addressing place-related queries is based on place names but has limited potential due to the vagueness of natural language and its tendency to lead to ambiguous interpretations. In previous work we proposed a system-ori...
This
chapter considers the importance of decision support systems
for supply chain risk management
(SCRM). The first part provides an overview of the different operations research techniques
and
methodologies
for decision making for managing risks, focusing on multiple-criteria decision
analysis methods and mathematical programming
. The second par...
Representation and reasoning over legal rules is an important application domain and a number of related approaches have been developed. In this work, we investigate legal reasoning in practice based on three use cases of increasing complexity. We consider three representation and reasoning approaches: (a) Answer Set Programming, (b) Argumentation...
Supply Chain Risk Management (SCRM) encompasses a wide variety of strategies aiming to identify, assess, mitigate and monitor unexpected events or conditions which might have an impact, mostly adverse, on any part of a supply chain. SCRM strategies often depend on rapid and adaptive decision making based on potentially large, multidimensional data...
Geographic Information Systems represent and process space whereas people refer to and use place. A question that arises is what are the benefits of introducing a unified data model that combines the rigid representation of space and the information-rich concepts of place. In this work we contribute to this research question by proposing a two-way...
The main underlying assumption of traditional legal knowledge representation and reasoning is that knowledge and data are both available in main memory. However, in the era of big data, where large amounts of data are generated daily, an increasing range of scientific disciplines, as well as business and human activities, are becoming data-driven....
Spatial information is often expressed using qualitative terms such as natural language expressions instead of coordinates; reasoning over such terms has several practical applications, such as bus routes planning. Representing and reasoning on trajectories is a specific case of qualitative spatial reasoning that focuses on moving objects and their...
Spatial information is often expressed using qualitative terms such as natural language expressions instead of coordinates; reasoning over such terms has several practical applications, such as bus routes planning. Representing and reasoning on trajectories is a specific case of qualitative spatial reasoning that focuses on moving objects and their...
Effective and accurate service discovery and composition rely on complete specifications of service behaviour, containing inputs and preconditions that are required before service execution, outputs, effects and ramifications of a successful execution and explanations for unsuccessful executions. The previously defined Web Service Specification Lan...
Service-Oriented Architecture (SOA) has emerged as a prominent design style that enables an IT infrastructure to allow different applications to participate in business processes, regardless of their underlying features, by encapsulating them as platform-independent entities that become available via a certain network, primarily the Internet. In or...
We address the problem of synthesizing specifications for composite Web services, starting from those of their component services. Unlike related work in programming languages, we assume the definition of the component services (i.e. their code) to be unavailable - at best, they are known by a specification which (safely) approximates their functio...
Two major interrelated issues are identified in the fields of service description and service composition: the lack of formal specifications for atomic services and service compositions and the inability of current service composition approaches to simultaneously satisfy requirements such as QoS-awareness, dynamicity and scalability in an effective...
In this paper, we identify two major issues related to Web service composition: the lack of formal specifications for services and service compositions and the inability of current service composition approaches to support dynamicity and QoS-awareness in an effective and scalable way. We analyze the underlying research challenges for each of these...
S-Cube’s Foundations for the Internet of Services Today’s Internet is standing at a crossroads. The Internet has evolved from a source of information to a critical infrastructure which underpins our lives and economies. The demand for more multimedia content, more interconnected devices, more users, a richer user experience, services available any...
In this work we present "AlertMe", a semantics-based, context-aware notification system that provides personalized alerts to graduate students based on their preferences. An extensive description of the system is carried out. We present the underlying ontology that models the available knowledge, as well as how higher level knowledge inference and...
Service-Oriented Architecture has emerged in recent years as a prominent design style that enables an IT infrastructure to allow different applications to exchange data and participate in business processes, regardless of the underlying complexity of the applications, such as the exact implementation or the operating systems or the programming lang...
This work explores the frame problem and its effects in devising Web service specifications. The frame problem encompasses the issues raised when trying to concisely state in a specification that nothing changes except when explicitly mentioned otherwise. A motivating example of a composite service specification is presented and a solution approach...
In this paper, the design and development of a web database for the purpose of storing and processing data produced by a complete building energy analysis is presented. Additionally, a web site that provides access to the database as well as a set of processing functions is outlined. The data stored are read from special input files produced by the...