@article {844, title = {AI and Related Technologies in the Fields of Smart Agriculture: A Review}, journal = {Information}, volume = {16}, year = {2025}, pages = {100}, abstract = {

The integration of cutting-edge technologies{\textemdash}such as the Internet of Things (IoT), artificial intelligence (AI), machine learning (ML), and various emerging technologies{\textemdash}is revolutionizing agricultural practices, enhancing productivity, sustainability, and efficiency. The objective of this study is to review the literature regarding the development and evolution of AI as well as other emerging technologies in the various fields of Agriculture as they are developed and transformed by integrating the above technologies. The areas examined in this study are open field smart farming, vertical and indoor farming, zero waste agriculture, precision livestock farming, smart greenhouses, and regenerative agriculture. This paper links current research, technological innovations, and case studies to present a comprehensive review of these emerging technologies being developed in the context of smart agriculture, for the benefit of farmers and consumers in general. By exploring practical applications and future perspectives, this work aims to provide valuable insights to address global food security challenges, minimize environmental impacts, and support sustainable development goals through the application of new technologies.

}, keywords = {AI, artificial intelligence, indoor farming, IoT, precision agriculture, precision livestock, regenerative agriculture, smart farming, smart green, vertical farming, zero-waste agriculture}, issn = {2078-2489}, doi = {https://doi.org/10.3390/info16020100}, author = {Assimakopoulos, Fotis and Vassilakis, Costas and Margaris, Dionisis and Kotis, Konstantinos and Spiliotopoulos, Dimitris} } @article {847, title = {Fine-Tuning Large Language Models for Ontology Engineering: A Comparative Analysis of GPT-4 and Mistral}, journal = {Applied Sciences}, volume = {15}, year = {2025}, month = {February}, pages = {2146}, abstract = {

Ontology engineering (OE) plays a critical role in modeling and managing structured knowledge across various domains. This study examines the performance of fine-tuned large language models (LLMs), specifically GPT-4 and Mistral 7B, in efficiently automating OE tasks. Foundational OE textbooks are used as the basis for dataset creation and for feeding the LLMs. The methodology involved segmenting texts into manageable chapters, generating question{\textendash}answer pairs, and translating visual elements into description logic to curate fine-tuned datasets in JSONL format. This research aims to enhance the models{\textquoteright} abilities to generate domain-specific ontologies, with hypotheses asserting that fine-tuned LLMs would outperform base models, and that domain-specific datasets would significantly improve their performance. Comparative experiments revealed that GPT-4 demonstrated superior accuracy and adherence to ontology syntax, albeit with higher computational costs. Conversely, Mistral 7B excelled in speed and cost efficiency but struggled with domain-specific tasks, often generating outputs that lacked syntactical precision and relevance. The presented results highlight the necessity of integrating domain-specific datasets to improve contextual understanding and practical utility in specialized applications, such as Search and Rescue (SAR) missions in wildfire incidents. Both models, despite their limitations, exhibited potential in understanding OE principles. However, their performance underscored the importance of aligning training data with domain-specific knowledge to emulate human expertise effectively. This study, based on and extending our previous work on the topic, concludes that fine-tuned LLMs with targeted datasets enhance their utility in OE, offering insights into improving future models for domain-specific applications. The findings advocate further exploration of hybrid solutions to balance accuracy and efficiency.

}, keywords = {domain-specific knowledge, large language models fine-tuning, ontology engineering, search and rescue}, issn = {2076-3417}, doi = {https://doi.org/10.3390/app15042146}, author = {Doumanas, Dimitrios and Soularidis, Andreas and Spiliotopoulos, Dimitris and Vassilakis, Costas and Kotis, Konstantinos} } @article {843, title = {A generalized, rule-based method for the detection of intermittent faults in software programs}, journal = {Journal of Systems and Software}, volume = {219}, year = {2025}, pages = {112228}, abstract = {

Intermittent faults are a very common problem in the software world, and are very hard to locate and correct, due to the fact that they manifest their presence only under certain circumstances. Most of the existing approaches for intermittent fault identification assume that suitable instrumentation has been provided in the program, typically in the form of assertions that dictate which program states are considered to be erroneous, by examining variable values. This approach is, however, inefficient, since only errors for which appropriate instrumentation has been provided will be uncovered. In this paper we propose a method that can be used to detect probable sources of intermittent faults within a program. Our method proposes certain points in the code, whose data interdependencies combined with their execution interweaving indicate that they could be the root cause of intermittent faults. The approach presented in this paper extends our previous work, by examining shared variable access sequences and taking into account not only direct dependencies between shared variables, but additionally indirect ones, i.e. cases where values of local variables are computed using values of some shared variable(s), and subsequently the local variable values are used to set the value of other shared variables. The detection of suspicious access pattern, which may indicate the presence of intermittent faults, is formalized through the introduction of generalized rules; these rules are combined with model-based checking to ensure that all program execution paths are covered. The list of suspicious locations within the code is finally presented to the developer, who will decide after a thorough examination of the code, to accept or reject each of the proposals.

}, keywords = {Fault detection, Intermittent faults, Model-based checking, Shared variables}, issn = {0164-1212}, doi = {10.1016/j.jss.2024.112228}, author = {Sotiropoulos, Panagiotis and Vassilakis, Costas} } @article {845, title = {Virtual Cultural Experiences for the Elderly: Formative Studies and First Findings Regarding Cultural Content}, journal = {Applied Sciences}, volume = {15}, year = {2025}, month = {February}, pages = {1820}, abstract = {

In societies with aging populations, the active participation of elders in all aspects of societal life is crucial. Cultural heritage provides a rich vehicle for engaging elders, stimulating both cognitive and affective responses, and keeping human brains active. This study focused on gathering requirements for creating content for virtual cultural experiences. Specifically, participants were shown various images and a film, and their self-reported thoughts and emotions were collected. The images were chosen by experts for the specific emotions they could trigger. The same images were analyzed with sentiment analysis software and finally given to the elders to express their emotions. The results of sentiment analysis, the analysis of experts, and the data from the participants were compared, showing differences between the perceived emotions. Regarding the historical film, the participants discussed the main emotions they experienced. The results were analyzed to extract guidelines for the content creation of VR applications.

}, keywords = {content requirements, elders, Virtual reality}, issn = {2076-3417}, doi = {10.3390/app15041820}, author = {Antoniou, Angeliki and Vassilakis, Costas and Kantianis, Georgios and Sylaiou, Stella and Kolokithas, George} } @article {842, title = {Artificial Intelligence Tools for the Agriculture Value Chain: Status and Prospects}, journal = {Electronics}, volume = {13}, year = {2024}, pages = {4362}, abstract = {

This article explores the transformative potential of artificial intelligence (AI) tools across the agricultural value chain, highlighting their applications, benefits, challenges, and future prospects. With global food demand projected to increase by 70\% by 2050, AI technologies{\textemdash}including machine learning, big data analytics, and the Internet of things (IoT){\textemdash}offer critical solutions for enhancing agricultural productivity, sustainability, and resource efficiency. The study provides a comprehensive review of AI applications at multiple stages of the agricultural value chain, including land use planning, crop selection, resource management, disease detection, yield prediction, and market integration. It also discusses the significant challenges to AI adoption, such as data accessibility, technological infrastructure, and the need for specialized skills. By examining case studies and empirical evidence, the article demonstrates how AI-driven solutions can optimize decision-making and operational efficiency in agriculture. The findings underscore AI{\textquoteright}s pivotal role in addressing global agricultural challenges, with implications for farmers, agribusinesses, policymakers, and researchers. This article aims to advance the evolving research and discussions on sustainable agriculture, contributing insights that promote the adoption of AI technologies and influence the future of farming.

}, keywords = {artificial intelligence, precision agriculture, sustainability, value chain}, issn = {2079-9292}, doi = {10.3390/electronics13224362}, author = {Assimakopoulos, Fotis and Vassilakis, Costas and Margaris, Dionisis and Kotis, Konstantinos and Spiliotopoulos, Dimitris} } @article {837, title = {Challenges and Trends in Student Evaluation of Teaching: Analysis of SET Data from the University of Peloponnese}, journal = {Information}, volume = {15}, year = {2024}, month = {09/2024}, pages = {576}, abstract = {

This study examines the effectiveness of Student Evaluations of Teaching at the University of Peloponnese, which has systematically collected anonymous evaluations since 2015. The analysis focused on participation rates, average scores, and the correlation between student evaluations and their academic performance. Participation rates were notably low, averaging 14.63\%, with postgraduate students showing higher rates (27.33\%) than undergraduates (10.77\%). The average SET scores were moderately high, with postgraduates rating their courses slightly better (M = 4.137) than undergraduates (M = 3.899). A weak positive correlation was found between course grades and evaluations among undergraduates, whereas no significant correlation was observed for postgraduates. These findings highlight challenges in using SETs as reliable measures of teaching effectiveness and suggest the need for improved participation and more comprehensive evaluation methods. The results provide insights into enhancing assessment practices and contribute to the broader discourse on the validity of student evaluations in higher education.

}, keywords = {Higher Education, quality assurance, SET}, issn = {2078-2489}, doi = {10.3390/info15090576}, author = {Papadogiannis, Ilias and Vassilakis, Costas and Wallace, Manolis and Katsis, Athanassios} } @article {841, title = {Exploiting Rating Prediction Certainty for Recommendation Formulation in Collaborative Filtering}, journal = {Big Data and Cognitive Computing}, volume = {8}, year = {2024}, pages = {53}, abstract = {

Collaborative filtering is a popular recommender system (RecSys) method that produces rating prediction values for products by combining the ratings that close users have already given to the same products. Afterwards, the products that achieve the highest prediction values are recommended to the user. However, as expected, prediction estimation may contain errors, which, in the case of RecSys, will lead to either not recommending a product that the user would actually like (i.e., purchase, watch, or listen) or to recommending a product that the user would not like, with both cases leading to degraded recommendation quality. Especially in the latter case, the RecSys would be deemed unreliable. In this work, we design and develop a recommendation algorithm that considers both the rating prediction values and the prediction confidence, derived from features associated with rating prediction accuracy in collaborative filtering. The presented algorithm is based on the rationale that it is preferable to recommend an item with a slightly lower prediction value, if that prediction seems to be certain and safe, over another that has a higher value but of lower certainty. The proposed algorithm prevents low-confidence rating predictions from being included in recommendations, ensuring the recommendation quality and reliability of the RecSys.

}, keywords = {Algorithm, collaborative filtering, confidence, Evaluation, Rating Predictions, Recommender Systems, Uncertainty}, issn = {2504-2289}, doi = {10.3390/bdcc8060053}, author = {Margaris, Dionisis and Sgardelis, Kiriakos and Spiliotopoulos, Dimitris and Vassilakis, Costas} } @article {836, title = {The Implementation of {\textquotedblleft}Smart{\textquotedblright} Technologies in the Agricultural Sector: A Review}, journal = {Information}, volume = {15}, year = {2024}, pages = {466}, abstract = {

The growing global population demands an increase in agricultural production and the promotion of sustainable practices. Smart agriculture, driven by advanced technologies, is crucial to achieving these goals. These technologies provide real-time information for crop monitoring, yield prediction, and essential farming functions. However, adopting intelligent farming systems poses challenges, including learning new systems and dealing with installation costs. Robust support is crucial for integrating smart farming into practices. Understanding the current state of agriculture, technology trends, and the challenges in technology acceptance is essential for a smooth transition to Agriculture 4.0. This work reports on the pivotal synergy of IoT technology with other research trends, such as weather forecasting and robotics. It also presents the applications of smart agriculture worldwide, with an emphasis on government initiatives to support farmers and promote global adoption. The aim of this work is to provide a comprehensive review of smart technologies for precision agriculture and especially of their adoption level and results on the global scale; to this end, this review examines three important areas of smart agriculture, namely field, greenhouse, and livestock monitoring.

}, keywords = {Agriculture 4.0, IoT, precision agriculture, smart farming}, issn = {2078-2489}, doi = {https://doi.org/10.3390/info15080466}, url = {https://www.mdpi.com/2078-2489/15/8/466}, author = {Assimakopoulos, Fotis and Vassilakis, Costas and Margaris, Dionisis and Kotis, Konstantinos and Spiliotopoulos, Dimitris} } @article {835, title = {On the Quality and Validity of Course Evaluation Questionnaires Used in Tertiary Education in Greece}, journal = {Trends in Higher Education}, volume = {3}, year = {2024}, pages = {221{\textendash}234}, abstract = {

In compliance with national legislation, Greek tertiary education institutions assess educational quality often using a standardized anonymous questionnaire completed by students. This questionnaire aims to independently evaluate various course components, including content organization, instructor quality, facilities, infrastructure, and grading methods. Despite widespread use across universities, the questionnaire{\textquoteright}s validity remains unexamined. This study addresses this gap by analyzing 48,000+ questionnaire responses from the University of the Peloponnese (2014{\textendash}2022), encompassing 68 undergraduate and graduate programs. Confirmatory factor analyses were used to assess the quality of the questionnaire, while exploratory factor analyses were used to assess the dimensions of the tool based on the data. Both analyses reveal shortcomings: confirmatory analysis detects strong correlations between supposedly different factors, and exploratory analysis identifies dimensions inconsistent with the expected structure. These findings question the questionnaire{\textquoteright}s quality and the validity of drawn conclusions, while additionally identifying opportunities for reducing the number of questions, which can contribute to increased questionnaire submission rates. Given its common use across Greek universities and its influence on shaping courses, urgent redesigning of the questionnaire for tertiary education evaluation is recommended.

}, keywords = {CFA, EFA, Higher Education, quality assurance}, doi = {https://doi.org/10.3390/higheredu3020013}, url = {https://www.mdpi.com/2813-4346/3/2/13}, author = {Papadogiannis, Ilias and Vassilakis, Costas and Wallace, Manolis and Katsis, Athanassios} } @conference {825, title = {Estimating \iSAX Parameters for Efficiency}, booktitle = {Proceedings of the 27th European Conference on Advances in Databases and Information Systems (ADBIS 2023)}, year = {2023}, abstract = {

\iSAX is considered one of the most efficient indexes for time series. Several parameters affect the construction of an \iSAX index: the sliding window size, the threshold value, the number of segments and the maximum cardinality, the last two being related to the SAX representation. In this paper (i) we consider the effect of each parameter on the efficiency of the \iSAX index, (ii) we evaluate the importance of each parameter, and, (iii) suggest how to optimize these parameters.

}, keywords = {Indexing, Parameter tuning, SAX, Time series}, author = {Tsoukalos, Michalis and Platis, Nikos and Vassilakis, Costas} } @conference {829, title = {Estimating \iSAX Parameters for Efficiency}, booktitle = {New Trends in Database and Information Systems (Proceedings of the 2023 European Conference on Advances in Databases and Information Systems / ADBIS 2023)}, year = {2023}, publisher = {Springer Nature Switzerland}, organization = {Springer Nature Switzerland}, address = {Cham}, abstract = {

\iSAX is considered one of the most efficient indexes for time series. Several parameters affect the construction of an \iSAX index: the sliding window size, the threshold value, the number of segments and the maximum cardinality, the last two being related to the SAX representation. In this paper (i) we consider the effect of each parameter on the efficiency of the \iSAX index, (ii) we evaluate the importance of each parameter, and, (iii) suggest how to optimize these parameters.

}, keywords = {Indexing, Parameter tuning, SAX, Time series}, isbn = {978-3-031-42941-5}, doi = {10.1007/978-3-031-42941-5_1}, author = {Tsoukalos, Michalis and Platis, Nikos and Vassilakis, Costas} } @article {814, title = {The additional testsuite framework: facilitating software testing and test management}, journal = {International Journal of Web Engineering and Technology}, volume = {TBD}, year = {2022}, abstract = {

In this paper, we present the Additional Testsuite Framework, a novel test suite management approach, which provides structures and instrumentation for the creation, maintenance, evolution and use of test suites for software programs. In particular, the tests can be maintained in a centralized repository, and are developed and maintained independently of specific versions of the associated software. Through the use of annotations, tests are categorized and distributed to the desired versions of the software. The presented framework also supports test-based development, dynamic/selective program builds, feature-based builds, testing in different environments and source code analysis. The Additional Testsuite Framework concept has been implemented and extensively evaluated, with the test cases notably including the JBoss EAP CE and OpenLiberty servers.

}, keywords = {Additional Testsuite Framework, Dynamic Testing, Feature-based testing, Internet application testing, Multiple Environment Testing, Multiversion Testing, Test program repository, Test-Driven Development, Testsuite Management}, author = {Sotiropoulos, Panagiotis and Vassilakis, Costas} } @article {797, title = {On Addressing the Low Rating Prediction Coverage in Sparse Datasets Using Virtual Ratings}, journal = {SN Computer Science}, volume = {2}, year = {2021}, month = {May}, pages = {255}, abstract = {

Collaborative filtering-based recommendation systems consider users{\textquoteright} likings and interests, articulated as ratings within a database to offer personalized recommendations. Unfortunately, many collaborative filtering datasets exhibit the {\textquoteleft}{\textquoteleft}grey sheep{\textquoteright}{\textquoteright} phenomenon, a state where no near neighbours can be found for certain users. This phenomenon is extremely frequent in datasets where users, on average, have rated only a small percentage of the available items, which are termed as sparse datasets. This paper addresses the {\textquoteleft}{\textquoteleft}grey sheep{\textquoteright}{\textquoteright} problem by proposing the virtual ratings concept and introduces an algorithm for virtual rating creation on the basis of actual ratings. The novelty behind this concept is that the introduction of the virtual ratings effectively reduces the user{\textendash}item rating matrix sparsity, thus alleviating the aforementioned problem. The proposed algorithm, which is termed as CFVR, has been extensively evaluated and the results show that it achieves to considerably improve the capability of a collaborative filtering system to formulate tailored recommendations for each user, when operating on sparse datasets, while at the same time improves rating prediction quality.

}, keywords = {collaborative filtering, Cosine Similarity, Evaluation, Pearson Correlation Coefficient, Recommender Systems, Sparse Datasets, Virtual ratings}, issn = {2661-8907}, doi = {10.1007/s42979-021-00668-8}, url = {https://doi.org/10.1007/s42979-021-00668-8}, author = {Margaris, Dionisis and Spiliotopoulos, Dimitris and Karagiorgos, Gregory and Vassilakis, Costas and Vasilopoulos, Dionysios} } @conference {799, title = {Cyber-Security Training Evaluation Metrics}, booktitle = {Proceedings of the 2021 IEEE International Conference on Cyber Security and Resilience}, year = {2021}, publisher = {IEEE}, organization = {IEEE}, abstract = {

Cyber-security training has evolved into an imperative need, aiming to provide cyber-security professionals with the knowledge and skills required to confront cyber-attacks that are increasing in number and sophistication. Training activities are typically associated with evaluation means, aimed to assess the extent to which the trainee has acquired the knowledge and skills whose development is targeted by the training programme, while cyber-security awareness and skill level evaluation means may be used to support additional security-related aspects of organizations. In this paper, we review trainee performance assessment metrics in cyber-security training, aiming to assist designers of cyber-security training activities to identify the mostprominent trainee performance assessment means for their training programmes, while additional research directions involving cyber-security training evaluation metrics are also identified.

}, keywords = {Cyber-ranges, Cyber-security training, Evaluation, Gamification, Scoring}, author = {Koutsouris, Nikolaos and Vassilakis, Costas and Kolokotronis, Nicholas} } @conference {809, title = {Digital applications for the preservation of historic memory: the case of the Kalamata digital museum (greek title: Ψηφιακές εφαρμογές για τη διατήρηση της ιστορικής μνήμης: Η περίπτωση του ψηφιακού μουσείου Καλαμάτας)}, booktitle = {4th PanHellenic Conference on Digital Cultural Heritage-EuroMed 2021}, year = {2021}, abstract = {

The evolution in technology and informatics have changed all aspects of our lives, including civilization-related ones. And while interdisciplinary research involving the scientific disciplines of human sciences and informatics was underdeveloped for many years, it seems that informatics and technology can constitute the catalyst for the rapid advancement of certain areas in human sciences. This has become more than apparent during the last decade, especially in museums and venues related to civilization, where the application of technology, both for the improvement of the visitors{\textquoteright} experience and for the support of scientists (archaeologists, museologists etc), is now taken as granted. In particular, the measures that have been applied during the last two years of the COVID19 pandemic, further accelerate the developments towards digitalization and switching to an online world. The "Kalamata 1821: roads of freedom" project was the starting point for the creation of the first mixed digital museum in the region of the Peloponnese; this museum is strongly related to the history of Greece, starting from the Revolution of 1821 and its relationship with Kalamata. This paper analyses the process of design, preparation and implementation of digital multimedia applications that are installed on the digital museum infrastructure. The applications have been designed to maximize the information that can be perceived within the limited time frame during of a museum visit, offering in parallel means to obtain some of the information presented and store them on a mobile device.

}, author = {Poulopoulos, Vassileios and Wallace, Manolis and Antoniou, Antzela and Vassilakis, Costas and Panagiotidi, Vayia and Zacharias, Nikolaos} } @inbook {796, title = {Handbook of Research on Developing a Post-Pandemic Paradigm for Virtual Technologies in Higher Education}, year = {2021}, publisher = {IGI Global}, organization = {IGI Global}, chapter = {Social Skills and Online Learning}, abstract = {

The concept of online teaching and learning is widely adopted by universities due to the pandemic of COVID-19, which forced them to shift from blending learning (including both direct and remote education) to exclusive online education. Technology has provided a multitude of tools which can prove very useful in the hands of people who want to serve such a great purpose as education. During the days of the pandemic, where people are isolated, virtual community and virtual learning technology brings people together to experience a sense of educational community in the virtual world. This chapter introduces the reader to the importance for social skill training of the learning communities during times that online learning is the sole type of education, exploring methodologies, tools and practices that can be used to counterbalance the deficiencies introduced by distance learning methods regarding social skill development.

}, keywords = {Active learning, Community of Inquiry, COVID-19, Digital Collaboration Platforms, Higher Education, Pandemic, Problem-Based Learning, Remote Learning, Social skills, Virtual Learning Community}, author = {Kriemadis, Athanasios and Kapnisi, Dimitra and Vassilakis, Costas and Spiliotopoulos Dimitris} } @conference {801, title = {An Interface for User-Centred Process and Correlation Between Large Datasets}, booktitle = {Proceedings of the 2021 International Conference on Human-Computer Interaction: Design, User Experience, and Usability: UX Research and Design}, year = {2021}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, address = {Cham}, abstract = {

Standard database query systems are designed to process data on a single installation only, and do not provide optimal solutions for cases that data from multiple sources need to be queried. In these cases, the sources may have different data schemata, data representations etc., necessitating extensive coding and data transformations to retrieve partial results and combine them to reach the desired outcome. Differences in schemata and representations may be subtle and remain unnoticed, leading to the production of erroneous results. The goal of this paper is to present an easy-to-use solution for the end users, enabling them to query data from a given set of databases through a single user interface. This user interface allows users to visualize database contents and query results, while facilities for uploading and validating the data are also accommodated. To demonstrate the applicability of our approach, a use case is presented where data from two different sources are uploaded into the system and thereafter the data from the two databases can be utilized in tandem. The usability evaluation involved software developers in free evaluation scenarios.

}, keywords = {big data, Multiple data sources, Query, Usability, user interface, Visualisation}, isbn = {978-3-030-78221-4}, doi = {https://doi.org/10.1007/978-3-030-78221-4_32}, author = {Spiliotopoulos, Dimitris and Giannakopoulos, Theodoros and Vassilakis, Costas and Wallace, Manolis and Lantzouni, Marina and Poulopoulos, Vassilis and Margaris, Dionisis} } @conference {800, title = {Understanding and mitigating banking trojans: From Zeus to Emotet}, booktitle = {Proceedings of the 2021 IEEE International Conference on Cyber Security and Resilience}, year = {2021}, publisher = {IEEE}, organization = {IEEE}, abstract = {

Banking Trojans came a long way in the past decade, and the recent case of Emotet showed their enduring relevance. The evolution of the modern computing landscape can be traced through Emotet and Zeus, both representative examples from the end of the past decade. As an example of earlier malware, Zeus only needed to employ simple anti-analysis techniques to stay undetected; while the more recent Emotet had to constantly evolve to stay a step ahead. Current host-based antimalware solutions face an increasing number of obstacles to perform their function. A multi-layer approach to network security is necessary for network-based intrusion response systems to secure modern networks of heterogeneous devices. A system based on a combination of a graphical network security model and a game theoretic model of cyber attacks was tested on a testbed with Windows machines infected with Trojans; experimental results showed that the proposed system effectively blocked Trojans{\textquoteright} network communications effectively preventing data leakage and yielding encouraging results for future work.

}, keywords = {Cyber security, Graphical security models, Intrusion response systems, Malware analysis, Reverse engineering}, author = {Grammatikakis, Konstantinos-Panagiotis and Koufos, Ioannis and Kolokotronis, Nicholas and Vassilakis, Costas and Shiaeles, Stavros} } @conference {802, title = {A User Interface for Personalising WS-BPEL Scenarios}, booktitle = {Proceedings of the 2021 International Conference on Human-Computer Interaction: HCI in Business, Government and Organizations}, year = {2021}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, address = {Cham}, abstract = {

Due to the huge volume of web services available, both locally and in the cloud, the performance of users and systems need significant research attention. Since WS-BPEL is the dominant language for orchestrating individual web services into business processes, by composing WS-BPEL scripts/scenarios, graphical notations facilitating WS-BPEL design can be extremely useful. Current user interfaces allow WS-BPEL designers not only to invoke selected web services, but also to explicitly ask for recommendations. Then, the user interface appends the services achieving the highest score, according to the attributes{\textquoteright} importance, set by the designer, to their WS-BPEL scenario. However, since the final selection is produced automatically, rather than relying on the designers{\textquoteright} choices, many times, from a personalisation point of view, the adaptation fails. This work reports on the design, development and user evaluation of a user interface that incorporates functionalities that support the designers{\textquoteright} selection performance, thereby upgrading the personalisation level of the WS-BPEL scenarios, as well as the success of the adaptation.

}, keywords = {Personalisation, Recommender Systems, Usability, User evaluation, user interface, Web services, WS-BPEL scenarios}, isbn = {978-3-030-77750-0}, doi = {https://doi.org/10.1007/978-3-030-77750-0_25}, author = {Margaris, Dionisis and Spiliotopoulos, Dimitris and Vasilopoulos, Dionysios and Vassilakis, Costas} } @article {749, title = {Human behaviour in multimodal interaction: main effects of civic action and interpersonal and problem-solving skills}, journal = {Journal of Ambient Intelligence and Humanized Computing}, volume = {TBD}, year = {2020}, month = {mar}, pages = {TBD}, abstract = {Metacognitive skill training may rest within any kind of social interaction that requires awareness of what an individual and others think, in social, educational and organizational settings alike. This work explores an extensive study of multimodal application interaction (virtual agent, spoken dialogue, visual communication of progress) for metacognitive skill training via negotiation skill training settings. Human behaviour, as effected by civic action and interpersonal and problem-solving skill training, is investigated through interaction sessions with a virtual agent on multimodal multiparty negotiation. This work reports on the results of the user-system evaluation sessions involving 41 participants before-and-after interaction with the system, integrating macro- (dialogue system performance) and micro- (metacognitive-related and individual-and-community level-related attitudes and skills) factors. Findings indicate significant and positive relationships between user and system evaluation questions after interaction with the dialogue system and between self-efficacy, self-regulation, individual readiness to change, mastery goal orientation, interpersonal and problem-solving skills and civic action before-and-after the interaction experience. Implications, limitations and further research issues are discussed in light of context of the multimodal interaction and its effects on the human behaviour during metacognitive skill training.}, keywords = {Human Behaviour, Individual- and Community-Level Related Attitudes and Skills, Metacognitive, Multimodal Interaction, Skill Training}, doi = {10.1007/s12652-020-01846-x}, author = {Markri, Eleni and Spiliotopoulos, Dimitris and Vassilakis, Costas and Margaris, Dionisis} } @inbook {780, title = {Reconnaisance}, booktitle = {Cyber-Security Threats, Actors, and Dynamic Mitigation}, year = {2020}, publisher = {CRC Press}, organization = {CRC Press}, abstract = {

Reconnaissance is the phase of gathering information about an organization{\textquoteright}s network and computing devices. This information includes both technical characteristics, such as IP addresses and software, and non-technical aspects, such as people operating the devices and the business value of tangible or intangible assets associated with the computing infrastructure. This information allows attackers to better understand their target and prepare elaborate attack plans. The reconnaissance phase is itself divided in a number of subphases, and each subphase is supported by a multitude of tools. In this chapter, we firstly describe in more detail the reconnaissance phase, while subsequently we present the different subphases and the methods and tools supporting each one, providing specific examples of how reconnaissance actions are performed. The information presented in this chapter can be used by organizations{\textquoteright} cyber-security officers to identify weaknesses in the organizations{\textquoteright} security plan implementations and take appropriate mitigation measures.

}, keywords = {Actors, Cyber-Security Threats, Dynamic Mitigation, Reconnaissance}, author = {Mathas, Christos-Minas and Vassilakis, Costas}, editor = {Nicholas Kolokotronis and Stavros Shiaeles} } @article {722, title = {Handling uncertainty in social media textual information for improving venue recommendation formulation quality in social networks}, journal = {Social Network Analysis and Mining}, volume = {9}, year = {2019}, month = {Oct}, pages = {64}, abstract = {One of the major problems that social media front is to continuously produce successful, user-targeted information, in the form of recommendations, which are produced by applying methods from the area of recommender systems. One of the most important applications of recommender systems in social networks is venue recommendation, targeted by the majority of the leading social networks (Facebook, TripAdvisor, OpenTable, etc.). However, recommender systems{\textquoteright} algorithms rely only on the existence of numeric ratings which are typically entered by users, and in the context of social networks, this information is scarce, since many social networks allow only reviews, rather than explicit ratings. Even if explicit ratings are supported, users may still resort to expressing their views and rating their experiences through submitting posts, which is the predominant user practice in social networks, rather than entering explicit ratings. User posts contain textual information, which can be exploited to compute derived ratings, and these derived ratings can be used in the recommendation process in the lack of explicitly entered ratings. Emerging recommender systems encompass this approach, without however tackling the fact that the ratings computed on the basis of textual information may be inaccurate, due to the very nature of the computation process. In this paper, we present an approach which extracts features of the textual information, a widely available source of information in venue category, to compute a confidence metric for the ratings that are computed from texts; then, this confidence metric is used in the user similarity computation and venue rating prediction formulation process, along with the computed rating. Furthermore, we propose a venue recommendation method that considers the generated venue rating predictions, along with venue QoS, similarity and spatial distance metrics in order to generate venue recommendations for social network users. Finally, we validate the accuracy of the rating prediction method and the user satisfaction from the recommendations generated by the recommendation formulation algorithm. Conclusively, the introduction of the confidence level significantly improves rating prediction accuracy, leverages the ability to generate personalized recommendations for users and increases user satisfaction.}, keywords = {collaborative filtering, Confidence level, Rating prediction, Recommender Systems, Social Networks, Textual information, Uncertainty, Venue recommendation formulation}, issn = {1869-5469}, doi = {10.1007/s13278-019-0610-x}, url = {https://doi.org/10.1007/s13278-019-0610-x}, author = {Margaris, Dionisis and Vassilakis, Costas and Spiliotopoulos, Dimitris} }