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Cyprus University of Technology, Cyprus

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30 Papers

IDEA: Index of Difficulty for Eye Tracking Applications - An Analysis Model for Target Selection Tasks

Topics: Analysis Methods and Tools; Cognitive and Conceptual Models; Mobile, Wearable and Pervasive Devices; Evaluation Paradigms and Frameworks; User Performance Evaluation; Usability and User Experience

Authors: Mohsen Parisay ¹ , Charalambos Poullis ¹ and Marta Kersten-Oertel ^{1

;

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Affiliations: ¹ Department of Computer Science, Concordia University, Montreal, Canada , ² PERFORM Center, Montreal, Canada

Digital Object Identifier: 10.5220/0010195701350144

Proceedings of HUCAPP 2021, ISBN: 978-989-758-488-6

Quick Abstract

Fitts’ law is a prediction model to measure the difficulty level of target selection for pointing devices. However, emerging devices and interaction techniques require more flexible parameters to adopt the original Fitts’ law to new circumstances and case scenarios. We propose Index of Difficulty for Eye tracking Applications (IDEA) which integrates Fitts’ law with users’ feedback from the NASA TLX to measure the difficulty of target selection. The COVID-19 pandemic has shown the necessity of contact-free interactions on public and shared devices, thus in this work, we aim to propose a model for evaluating contact-free interaction techniques, which can accurately measure the difficulty of eye tracking applications and can be adapted to children, users with disabilities, and elderly without requiring the acquisition of physiological sensory data. We tested the IDEA model using data from a three-part user study with 33 participants that compared two eye tracking selection techniques, dwell-time, and a multi-modal eye tracking technique using voice commands.

A Smart Product Co-design and Monitoring Framework Via Gamification and Complex Event Processing

Topics: Software Engineering; Simulation; Tools, Techniques and Methodologies for System Development; Model Driven Architectures and Engineering; Internet of Things

Authors: Spyros Loizou ¹ , Amal Elgammal ² , Indika Kumara ³ , Panayiotis Christodoulou ¹ , Mike Papazoglou ³ and Andreas Andreou ¹

Affiliations: ¹ Department of Computer Engineering and Informatics, Cyprus University of Technology, Limassol and Cyprus , ² Department of Computers & Information, Cairo University and Egypt , ³ Department of Economics and Management, Tilburg University and The Netherlands

Digital Object Identifier: 10.5220/0007720902370244

Proceedings of ICEIS 2019, ISBN: 978-989-758-372-8

Quick Abstract

In the traditional software development cycle, requirements gathering is considered the most critical phase. Getting the requirements right early has become a dogma in software engineering because the correction of erroneous or incomplete requirements in later software development phases becomes overly expensive. For product-service systems (PSS), this dogma and standard requirements engineering (RE) approaches are not appropriate because classical RE is considered concluded once a product service is delivered. This paper proposes a novel framework that enables the customer and the product engineer to co-design smart products by integrating three novel and advanced technologies to support: view-based modelling, visualization and monitoring, i.e., Product-Oriented Configuration Language (PoCL), gamification and Complex Event Processing (CEP), respectively. These create a “digital-twin” model of the connected ‘smart’ factory of the future. The framework is formally founded on the novel concept of manufacturing blueprints, which are formalized knowledge-intensive structures that provide the basis for actionable PSS and production “intelligence” and a move toward more fact-based manufacturing decisions. Implementation and validation of the proposed framework through real-life case studies are ongoing to validate the applicability, utility and efficacy of the proposed solutions.

Related Ontology Subjects/Areas/Topics (18)

Agents	Information Systems Analysis and Specification	Simulation
Artificial Intelligence	Internet of Things	Software Agents and Internet Computing
Bioinformatics	Methodologies and Technologies	Software and Architectures
Biomedical Engineering	Model Driven Architectures and Engineering	Software Engineering
Data Communication Networking	Operational Research	Telecommunications
Enterprise Information Systems	Sensor Networks	Tools, Techniques and Methodologies for System Development

Migration of Software Components to Microservices: Matching and Synthesis

Topics: Component-Based Software Engineering; Software and Systems Development Methodologies; Cloud Computing; Service-Oriented Architectures (SOA); Delivery, Deployment and Maintenance of Service Systems; Service Level Agreements (Slas) and Quality of Service (Qos); Service-Oriented Software Engineering

Authors: Andreas Christoforou , Lambros Odysseos and Andreas Andreou

Affiliation: Department of Electrical Engineering / Computer Engineering and Informatics, Cyprus University of Technology 31 Archbishop Kyprianos Street, Limassol and Cyprus

Digital Object Identifier: 10.5220/0007732101340146

Proceedings of ENASE 2019, ISBN: 978-989-758-375-9

Quick Abstract

Nowadays more and more software companies, as well as individual software developers, adopt the microservice architecture for their software solutions. Although many software systems are being designed and developed from scratch, a significant number of existing monolithic solutions tend to be transformed to this new architectural style. What is less common, though, is how to migrate component-based software systems to systems composed of microservices and enjoy the benefits of ease of changes, rapid deployment and versatile architecture. This paper proposes a novel and integrated process for the decomposition of existing software components with the aim being to fully or partially replace their functional parts with by a number of suitable and available microservices. The proposed process is built on semi-formal profiling and utilizes ontologies to match between properties of the decomposed functions of the component and those offered by microservices residing in a repository. Matching concludes with recommended solutions yielded by multi-objective optimization which considers also possible dependencies between the functional parts.

Related Ontology Subjects/Areas/Topics (11)

Agents	Component-Based Software Engineering	Service-Oriented Software Engineering and Management
Applications and Software Development	Data Engineering	Software and Systems Development Methodologies
Artificial Intelligence	Databases and Data Security	Software Engineering
Cloud Computing	Model-Driven Software Development

Smart Shop-floor Monitoring via Manufacturing Blueprints and Complex-event Processing

Topics: Software Engineering; Simulation; Tools, Techniques and Methodologies for System Development; Model Driven Architectures and Engineering; Internet of Things

Authors: Michalis Pingos ¹ , Amal Elgammal ² , Indika Kumara ³ , Panayiotis Christodoulou ¹ , Mike Papazoglou ³ and Andreas Andreou ¹

Digital Object Identifier: 10.5220/0007720802290236

Proceedings of ICEIS 2019, ISBN: 978-989-758-372-8

Quick Abstract

Nowadays, Product-Service-Systems (PSS) are being modernized into smart connected products that target to transform the industrial scenery and unlock unique prospects. This concept enforces a new technological heap and lifecycle models to support smart connected products. The intelligence that smart, connected products embed paves the way for more sophisticated data gathering and analytics capabilities ushering in tandem a new era of smarter supply and production chains, smarter production processes, and even end-to-end connected manufacturing ecosystems. The main contribution of this paper is a smart shop-floor monitoring framework and underpinning technological solutions, which enables the proactive identification and resolution of shop-floor distributions. The proposed monitoring framework is based on the synergy between the novel concept of Manufacturing Blueprints and Complex Event Processing (CEP) technologies, while it encompasses a middleware layer that enables loose coupling and adaptation in practice. The framework provides the basis for actionable PSS and production “intelligence” and facilitates a shift toward more fact-based manufacturing decisions. Implementation and validation of the proposed framework is performed through a real-world case study which demonstrates its applicability, and assesses the usability and efficiency of the proposed solutions.

Related Ontology Subjects/Areas/Topics (18)

Agents	Information Systems Analysis and Specification	Simulation
Artificial Intelligence	Internet of Things	Software Agents and Internet Computing
Bioinformatics	Methodologies and Technologies	Software and Architectures
Biomedical Engineering	Model Driven Architectures and Engineering	Software Engineering
Data Communication Networking	Operational Research	Telecommunications
Enterprise Information Systems	Sensor Networks	Tools, Techniques and Methodologies for System Development

A Hybrid Prediction Model Integrating Fuzzy Cognitive Maps with Support Vector Machines

Topic: Problem Solving

Authors: Panayiotis Christodoulou , Andreas Christoforou and Andreas S. Andreou

Affiliation: Cyprus University of Technology, Cyprus

Digital Object Identifier: 10.5220/0006329405540564

Proceedings of ICEIS 2017, ISBN: 978-989-758-247-9

Quick Abstract

This paper introduces a new hybrid prediction model combining Fuzzy Cognitive Maps (FCM) and Support Vector Machines (SVM) to increase accuracy. The proposed model first uses the FCM part to discover correlation patterns and interrelationships that exist between data variables and form a single latent variable. It then feeds this variable to the SVM part to improve prediction capabilities. The efficacy of the hybrid model is demonstrated through its application on two different problem domains. The experimental results show that the proposed model is better than the traditional SVM model and also outperforms other widely used supervised machine-learning techniques like Weighted k-NN, Linear Discrimination Analysis and Classification Trees.

Related Ontology Subjects/Areas/Topics (3)

Artificial Intelligence and Decision Support Systems

Enterprise Information Systems

Problem Solving

A Long-range Vision System for Projection Mapping of Stereoscopic Content in Outdoor Areas

Topics: Augmented, Mixed and Virtual Environments; Augmented, Mixed and Virtual Environments; Collaborative Augmented, Mixed and Virtual Environments; Lighting and Appearance; Surface Modeling; Procedural and interactive artworks

Authors: Behnam Maneshgar , Leila Sujir , Sudhir P. Mudur and Charalambos Poullis

Affiliation: Concordia University, Canada

Digital Object Identifier: 10.5220/0006258902900297

Proceedings of GRAPP 2017/VISIGRAPP, ISBN: 978-989-758-224-0

Quick Abstract

Spatial Augmented Reality, or its more commonly known name Projection Mapping (PM), is a projection technique which transforms a real-life object or scene into a surface for video projection (Raskar et al., 1998b). Although this technique has been pioneered and used by Disney since the seventies, it is in recent years that it has gained significant popularity due to the availability of specialized software which simplifies the otherwise cumbersome calibration process (Raskar et al., 1998a). Currently, PM is being widely used in advertising, marketing, cultural events, live performances, theater, etc as a way of enhancing an object/scene by superimposing visual content (Ridel et al., 2014). However, despite the wide availability of specialized software, several restrictions are still imposed on the type of objects/scenes on which PM can be applied. Most limitations are due to problems in handling objects/scenes with (a) complex reflectance properties and (b) low intensity or distinct colors. In this work, we address these limitations and present solutions for mitigating these problems. We present a complete framework for calibration, geometry acquisition and reconstruction, estimation of reflectance properties, and finally color compensation; all within the context of outdoor long-range PM of stereoscopic content. Using the proposed technique, the observed projections are as close as possible [constrained by hardware limitations] to the actual content being projected; therefore ensuring the perception of depth and immersion when viewed with stereo glasses. We have performed extensive experiments and the results are reported.

Related Ontology Subjects/Areas/Topics (7)

Augmented, Mixed and Virtual Environments	Interactive Environments	Rendering
Computer Vision, Visualization and Computer Graphics	Lighting and Appearance	Surface Modeling
Geometry and Modeling

A Real-time Targeted Recommender System for Supermarkets

Topic: User Profiling and Recommender Systems

Authors: Panayiotis Christodoulou ¹ , Klitos Christodoulou ² and Andreas S. Andreou ¹

Affiliations: ¹ Cyprus University of Technology, Cyprus , ² Neapolis University Pafos, Cyprus

Digital Object Identifier: 10.5220/0006309907030712

Proceedings of ICEIS 2017, ISBN: 978-989-758-248-6

Quick Abstract

Supermarket customers find it difficult to choose from a large variety of products or be informed for the latest offers that exist in a store based on the items that they need or wish to purchase. This paper presents a framework for a Recommender System deployed in a supermarket setting with the aim of suggesting real-time personalized offers to customers. As customers navigate in a store, iBeacons push personalized notifications to their smart-devices informing them about offers that are likely to be of interest. The suggested approach combines an Entropy-based algorithm, a Hard k-modes clustering and a Bayesian Inference approach to notify customers about the best offers based on their shopping preferences. The proposed methodology improves the customer's overall shopping experience by suggesting personalized items with accuracy and efficiency. Simultaneously, the properties of the underlying techniques used by the proposed framework tackle the data sparsity, the cold-start problem and other scalability issues that are often met in Recommender Systems. A preliminary setup in a local supermarket confirms the validity of the proposed methodology, in terms of accuracy, outperforming the traditional Collaborative Filtering approaches of user-based and item-based.

Related Ontology Subjects/Areas/Topics (5)

Artificial Intelligence		Knowledge-Based Systems		User Profiling and Recommender Systems
Knowledge Discovery and Information Retrieval		Symbolic Systems

Automatic Matching of Software Component Requirements using Semi-formal Specifications and a CBSE Ontology

Topics: Application Integration Technologies; Software and Systems Development Methodologies

Authors: Andreas S. Andreou ¹ and Efi Papatheocharous ²

Affiliations: ¹ Cyprus University of Technology, Cyprus , ² Swedish Institute of Computer Science, Sweden

Digital Object Identifier: 10.5220/0005378301180128

Proceedings of ENASE 2015, ISBN: 978-989-758-100-7

Quick Abstract

One of the most significant tasks of component-based software development is concerned with finding suitable components for integration. This paper introduces a novel development framework that promotes reusability and focuses on assessing the suitability level of candidate components. A specifications profile is first created using a semi-formal natural language that describes the desired functional and non-functional properties of the component(s) sought. A parser automatically recognizes parts of the profile and translates them into instance values of a dedicated CBSE ontology, the latter addressing issues of components’ reusability. Available components on the market are also stored as instances of the CBSE ontology. Matching between required and offered component properties takes place automatically at the level of the ontology items and a suitability ratio is calculated that suggests which components to consider for integration.

Related Ontology Subjects/Areas/Topics (5)

Application Integration Technologies		Service-Oriented Software Engineering and Management		Software Engineering
Applications		Software and Systems Development Methodologies

A Fuzzy Cognitive Map System to Explore Certain Scenarios on the Cyprus Banking System

Topics: Soft Computing and Intelligent Agents; Complex Fuzzy Systems; Fuzzy Systems Design, Modeling and Control

Authors: Maria Papaioannou ¹ , Costas Neocleous ² , Charalambos Papageorgiou ³ and Christos N. Schizas ¹

Affiliations: ¹ University of Cyprus, Cyprus , ² Cyprus University of Technology, Cyprus , ³ European University of Cyprus, Cyprus

Digital Object Identifier: 10.5220/0005039701030110

Proceedings of FCTA 2014/IJCCI, ISBN: 978-989-758-053-6

Quick Abstract

The model of Fuzzy Cognitive Maps (FCMs) allows a user to investigate how the influencing parameters of a cause-effect system behave under the implementation of desired scenario. Human knowledge and experience is used to define the structure and the parameters of a FCM model. This paper proposes a methodology which smoothly directs the steps that the experts should take in building an FCM system in order to reduce the subjectivity which characterizes such expert systems. Furthermore, an update rule for the parameters during the simulation phase is presented. Finally, the novel FCM construction methodology and the proposed FCM update rule are tested on a real-life system to investigate the repercussions of the combination of the Greek private sector involvement (PSI) with the decrease of people’s confidence towards the Cyprus banking system. The results resemble what actually happened to the Cyprus economy a short period after the implementation of the Greek PSI.

Related Ontology Subjects/Areas/Topics (7)

Artificial Intelligence	Fuzzy Systems	Soft Computing
Complex Fuzzy Systems	Fuzzy Systems Design, Modeling and Control	Soft Computing and Intelligent Agents
Computational Intelligence

On the Claim for the Existence of “Adversarial Examples” in Deep Learning Neural Networks

Topic: Neural Computation Issues in Social Behaviour Emergence

Authors: Costas Neocleous ¹ and Christos N. Schizas ²

Affiliations: ¹ Cyprus University of Technology, Cyprus , ² University of Cyprus, Cyprus

Digital Object Identifier: 10.5220/0005152503060309

Proceedings of NCTA 2014/IJCCI, ISBN: 978-989-758-054-3

Quick Abstract

A recent article in which it is claimed that adversarial examples exist in deep artificial neural networks (ANN) is critically examined. The newly discovered properties of ANNs are critically evaluated. Specifically, we point that adversarial examples can be serious problems in critical applications of pattern recognition. Also, they may stall the further development of artificial neural networks. We challenge the absolute existence of these examples, as this has not been universally proven yet. We also suggest that ANN structures, that correctly recognize adversarial examples, can be developed.

Related Ontology Subjects/Areas/Topics (17)

Artificial Intelligence	Methodologies and Methods	Physiological Computing Systems
Biomedical Engineering	Neural Computation Issues in Social Behaviour Emergence	Sensor Networks
Biomedical Signal Processing	Neural Networks	Signal Processing
Computational Intelligence	Neurocomputing	Soft Computing
Health Engineering and Technology Applications	Neurotechnology, Electronics and Informatics	Theory and Methods
Human-Computer Interaction	Pattern Recognition