loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Affiliation

Cyprus University of Technology, Cyprus
Context (please use complete words only)
Field
Sort by

38 Papers

Integrating Data Lakes with Self-Adaptive Serious Games

Topics: Meta Programming Systems and Meta Modeling; Frameworks and Middleware in Software Development; Big Data for SSE; SSE for Ontologies and Knowledge Management; SSE for Games and Gamification; Software Developments for Science and Research

Authors: Michalis Pingos , Spyros Loizou and Andreas Andreou

Affiliation: Department of Computer Engineering and Informatics, Cyprus University of Technology, Limassol, Cyprus

Digital Object Identifier: 10.5220/0013467700003928

Proceedings of ENASE 2025, ISBN: 978-989-758-742-9

Collapse Button Quick Abstract
Big Data challenges traditional data management tools due to their size, speed of generation, and variety of formats. The application of Big Data has become essential in areas like serious games, where it enhances functionality and effectiveness. Serious games benefit significantly from Big Data analytics, allowing for real-time data collection, processing and analysis of a vast number of users/players and their interactions. Traditional data management systems strive to handle the complexity of Big Data, particularly in environments like serious games, where diverse data sources create heterogeneity. This paper presents an approach that employs Data Lakes and semantic annotation as a solution for providing a scalable, flexible storage system for raw and unstructured data, enabling real-time processing and efficient management of Big Data produced by serious games. The effectiveness of the proposed approach is demonstrated through a speech therapy game example developed for the purposes of this study. A qualitative evaluation and comparison with two rival approaches is also performed using a set of criteria introduced in this work. The proposed approach offers an effective solution for handling data in multi-user gaming environments thus enhancing adaptability, personalization, and functional flexibility of serious games, and driving better user engagement and outcomes.

Enhancing Interaction with Data Lakes Using Digital Twins and Semantic Blueprints

Topics: SSE for Internet of Things; SSE for Ontologies and Knowledge Management; SSE for Games and Gamification

Authors: Spyros Loizou , Michalis Pingos and Andreas Andreou

Affiliation: Department of Computer Engineering and Informatics, Cyprus University of Technology, Limassol, Cyprus

Digital Object Identifier: 10.5220/0012620600003687

Proceedings of ENASE 2024, ISBN: 978-989-758-696-5

Collapse Button Quick Abstract
Advanced analytical techniques and sophisticated decision-making strategies are imperative for handling extensive volumes of data. As the quantity, diversity, and speed of data increase, there is a growing lack of confidence in the analytics process and resulting decisions. Despite recent advancements, such as metadata mechanisms in Big Data Processing and Systems of Deep Insight, effectively managing the vast and varied data from diverse sources remains a complex and unresolved challenge. Aiming to enhance interaction with Data Lakes, this paper introduces a framework based on a specialized semantic enrichment mechanism centred around data blueprints. The proposed framework takes into account unique characteristics of the data, guiding the process of locating sources and retrieving data from Data Lakes. More importantly, it facilitates end-user interaction without the need for programming skills or database management techniques. This is performed using Digital Twin functionality which offers model-based simulations and data-driven decision support.

Transforming Data Lakes to Data Meshes Using Semantic Data Blueprints

Topics: Meta Programming Systems and Meta Modeling; Software and System Interoperability; Big Data for SSE; SSE for Ontologies and Knowledge Management

Authors: Michalis Pingos , Athos Mina and Andreas Andreou

Affiliation: Department of Computer Engineering and Informatics, Cyprus University of Technology, Limassol, Cyprus

Digital Object Identifier: 10.5220/0012620200003687

Proceedings of ENASE 2024, ISBN: 978-989-758-696-5

Collapse Button Quick Abstract
In the continuously evolving and growing landscape of Big Data, a key challenge lies in the transformation of a Data Lake into a Data Mesh structure. Unveiling a transformative approach through semantic data blueprints enables organizations to align with changing business needs swiftly and effortlessly. This paper delves into the intricacies of detecting and shaping Data Domains and Data Products within Data Lakes and proposes a standardized methodology that combines the principles of Data Blueprints with Data Meshes. Essentially, this work introduces an innovative standardization framework dedicated to generating Data Products through a mechanism of semantic enrichment of data residing in Data Lakes. This mechanism not only enables the creation readiness and business alignment of Data Domains, but also facilitates the extraction of actionable insights from software products and processes. The proposed approach is qualitatively assessed using a set of functional attributes and is compared against established data structures within storage architectures yielding very promising results.

Visual Perception of Obstacles: Do Humans and Machines Focus on the Same Image Features?

Topics: Machine Learning Technologies for Vision; Deep Learning for Visual Understanding ; Mobile and Egocentric Object Detection and Recognition

Authors: Constantinos Kyriakides 1 , Marios Thoma 1 ; 2 , Zenonas Theodosiou 1 ; 3 , Harris Partaourides 4 , Loizos Michael 2 ; 1 and Andreas Lanitis 5 ; 1

Affiliations: 1 CYENS Centre of Excellence, Nicosia, Cyprus , 2 Open University of Cyprus, Nicosia, Cyprus , 3 Department of Communication and Internet Studies, Cyprus University of Technology, Limassol, Cyprus , 4 AI Cyprus Ethical Novelties Ltd, Limassol, Cyprus , 5 Department of Multimedia and Graphic Arts, Cyprus University of Technology, Limassol, Cyprus

Digital Object Identifier: 10.5220/0012453500003660

Proceedings of VISAPP 2024, ISBN: 978-989-758-679-8

Collapse Button Quick Abstract
Contemporary cities are fractured by a growing number of barriers, such as on-going construction and infrastructure damages, which endanger pedestrian safety. Automated detection and recognition of such barriers from visual data has been of particular concern to the research community in recent years. Deep Learning (DL) algorithms are now the dominant approach in visual data analysis, achieving excellent results in a wide range of applications, including obstacle detection. However, explaining the underlying operations of DL models remains a key challenge in gaining significant understanding on how they arrive at their decisions. The use of heatmaps that highlight the focal points in input images that helped the models reach their predictions has emerged as a form of post-hoc explainability for such models. In an effort to gain insights into the learning process of DL models, we studied the similarities between heatmaps generated by a number of architectures trained to detect obstacles on sidewalks in images collected via smartphones, and eye-tracking heatmaps generated by humans as they detect the corresponding obstacles on the same data. Our findings indicate that the focus points of humans more closely align with those of a Vision Transformer architecture, as opposed to the other network architectures we examined in our experiments.

Explainable Decision Support Modelling Based on Multi-Layer FCM with Multi-Objective Optimization Characteristics: The Case of the Microservices Adoption Problem

Topics: Knowledge Representation and Reasoning; Cognitive Systems; Evolutionary Computing; Fuzzy Systems; Explainable Artificial Intelligence

Authors: Andreas Christoforou

Affiliation: Department of Electrical Engineering, Computer Engineering and Informatics, Cyprus University of Technology, Limassol, Cyprus

Digital Object Identifier: 10.5220/0011681000003393

Proceedings of ICAART 2023, ISBN: 978-989-758-623-1

Collapse Button Quick Abstract
The tremendous progress in the field of artificial and computational intelligence has enabled the application of relevant techniques to a wide range of human life aspects. However, these techniques appear in their majority incompetent to allow users to explain and understand their decisions. This paper introduces an enhanced, explainable decision support approach using a promising graph-based computational intelligent model, namely Multi-Layer Fuzzy Cognitive Maps (MLFCM). MLFCM have evolved over the last two decades into a flexible and powerful tool that enables the execution of simulation scenarios to facilitate decision support in highly complex environments. The proposed enhancement of MLFCM revolves around their integration with MultiObjective Evolutionary Algorithms that allows executing simulations with multiple conflicting targets and then analyzing the values and relationships of the participating nodes. The applicability of the enhanced MLFCM is demonstrated through a case-study on adopting microservices. Microservices have been considered as one of the most promising alternatives to software development nowadays. Nevertheless, their adoption often stumbles on various factors such as security, exit policy, effectiveness, etc. In this context, the factors contributing to Microservices adoption are assessed, analyzed and modeled via MLFCM using a series of real-world and synthetic scenarios that yielded quite promising results.

A Data Lake Metadata Enrichment Mechanism via Semantic Blueprints

Topics: Meta Programming Systems and Meta Modeling; Software and System Interoperability; Big Data for SSE

Authors: Michalis Pingos and Andreas S. Andreou

Affiliation: Department of Computer Engineering and Informatics, Cyprus University of Technology, Limassol, Cyprus

Digital Object Identifier: 10.5220/0011080400003176

Proceedings of ENASE 2022, ISBN: 978-989-758-568-5

Collapse Button Quick Abstract
One of the greatest challenges in Smart Big Data Processing nowadays revolves around handling multiple heterogeneous data sources that produce massive amounts of structured, semi-structured and unstructured data through Data Lakes. The latter requires a disciplined approach to collect, store and retrieve/ analyse data to enable efficient predictive and prescriptive modelling, as well as the development of other advanced analytics applications on top of it. The present paper addresses this highly complex problem and proposes a novel standardization framework that combines mainly the 5Vs Big Data characteristics, blueprint ontologies and Data Lakes with ponds architecture, to offer a metadata semantic enrichment mechanism that enables fast storing to and efficient retrieval from a Data Lake. The proposed mechanism is compared qualitatively against existing metadata systems using a set of functional characteristics or properties, with the results indicating that it is indeed a promising approach.

Using Student Action Recognition to Enhance the Efficiency of Tele-education

Topics: Event and Human Activity Recognition; Video Surveillance and Event Detection

Authors: Eleni Dimitriadou 1 and Andreas Lanitis 2 ; 1

Affiliations: 1 Visual Media Computing Lab, Deptartment of Multimedia and Graphic Arts, Cyprus University of Technology, Cyprus , 2 CYENS - Centre of Excellence, Nicosia, Cyprus

Digital Object Identifier: 10.5220/0010868200003124

Proceedings of VISAPP 2022/VISIGRAPP, ISBN: 978-989-758-555-5

Collapse Button Quick Abstract
Due to the COVID-19 pandemic, many schools worldwide are using tele-education for class delivery. However, this causes a problem related to students’ active class participation. We propose to address the problem with a system that recognizes student’s actions and informs the teacher accordingly, while preserving the privacy of students. In the proposed action recognition system, seven typical actions performed by students attending online courses, are recognized using Convolutional Neural Network (CNN) architectures. The actions considered were defined by considering the relevant literature and educator’s views, and ensure that they provide information about the physical presence, active participation, and distraction of students, that constitute important pedagogical aspects of class delivery. The action recognition process is performed locally on the device of each student, thus it is imperative to use classification methods that require minimal computational load and memory requirements. Initial experimental results indicate that the proposed action recognition system provides promising classification results, when dealing with new instances of previously enrolled students or when dealing with previously unseen students.

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 ; 2

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/VISIGRAPP, ISBN: 978-989-758-488-6

Collapse Button 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 First-person Database for Detecting Barriers for Pedestrians

Topics: Understanding from Wearable and Mobile Cameras; Mobile and Egocentric Object Detection and Recognition; Mobile Vision; First Person Vision

Authors: Zenonas Theodosiou 1 , Harris Partaourides 1 , Tolga Atun 1 , Simoni Panayi 1 and Andreas Lanitis 2 ; 1

Affiliations: 1 Research Centre on Interactive Media Smart Systems and Emerging Technologies, Nicosia, Cyprus , 2 Department of Multimedia and Graphic Arts, Cyprus University of Technology, Limassol, Cyprus

Digital Object Identifier: 10.5220/0009107506600666

Proceedings of VISAPP 2020/VISIGRAPP, ISBN: 978-989-758-402-2

Collapse Button Quick Abstract
Egocentric vision, which relates to the continuous interpretation of images captured by wearable cameras, is increasingly being utilized in several applications to enhance the quality of citizens life, especially for those with visual or motion impairments. The development of sophisticated egocentric computer vision techniques requires automatic analysis of large databases of first-person point of view visual data collected through wearable devices. In this paper, we present our initial findings regarding the use of wearable cameras for enhancing the pedestrians safety while walking in city sidewalks. For this purpose, we create a first-person database that entails annotations on common barriers that may put pedestrians in danger. Furthermore, we derive a framework for collecting visual lifelogging data and define 24 different categories of sidewalk barriers. Our dataset consists of 1796 annotated images covering 1969 instances of barriers. The analysis of the dataset by means of object classification algorithms, depict encouraging results for further study.

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 S. Christoforou , Lambros Odysseos and Andreas S. 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

Collapse Button 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.
Collapse Button 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

Topic(s):

Filter listed results, by

Publication date

From:
To:
Author
Locate Author in list below
Data pager
Data pager
1234567
 Page 1 of 7, items 1 to 10 of 68.
Add Author(s) to filter:
Conference
Locate Conference in list below
Add Conference(s) to filter:

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic