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Costas Vassilakis is currently a Professor in the Department of Informatics and Telecommunications of the University of the Peloponnese. He holds a degree from the Department of Informatics of the University of Athens and a Ph.D. from the same department. He has published over 200 scientific papers in international scientific journals and conferences and has participated in more than 30 European and national research and development projects. He has served as PC member and referee in several international journals and conferences. His research interests include information systems, system architectures, computer security, virtual and mixed reality systems, semantic web technologies and applications and cultural informatics.
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.
Dimitris Spiliotopoulos; Τheodoros Giannakopoulos; Costas Vassilakis; Manolis Wallace; Marina Lantzouni; Vassilis Poulopoulos; Dionisis Margaris. An Interface for User-Centred Process and Correlation Between Large Datasets. Algorithms and Data Structures 2021, 477 -494.
AMA StyleDimitris Spiliotopoulos, Τheodoros Giannakopoulos, Costas Vassilakis, Manolis Wallace, Marina Lantzouni, Vassilis Poulopoulos, Dionisis Margaris. An Interface for User-Centred Process and Correlation Between Large Datasets. Algorithms and Data Structures. 2021; ():477-494.
Chicago/Turabian StyleDimitris Spiliotopoulos; Τheodoros Giannakopoulos; Costas Vassilakis; Manolis Wallace; Marina Lantzouni; Vassilis Poulopoulos; Dionisis Margaris. 2021. "An Interface for User-Centred Process and Correlation Between Large Datasets." Algorithms and Data Structures , no. : 477-494.
Dionisis Margaris; Dimitris Spiliotopoulos; Dionysios Vasilopoulos; Costas Vassilakis. A User Interface for Personalising WS-BPEL Scenarios. Transactions on Petri Nets and Other Models of Concurrency XV 2021, 399 -416.
AMA StyleDionisis Margaris, Dimitris Spiliotopoulos, Dionysios Vasilopoulos, Costas Vassilakis. A User Interface for Personalising WS-BPEL Scenarios. Transactions on Petri Nets and Other Models of Concurrency XV. 2021; ():399-416.
Chicago/Turabian StyleDionisis Margaris; Dimitris Spiliotopoulos; Dionysios Vasilopoulos; Costas Vassilakis. 2021. "A User Interface for Personalising WS-BPEL Scenarios." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 399-416.
The 5G communication network will underpin a vast number of new and emerging services, paving the way for unprecedented performance and capabilities in mobile networks. In this setting, the Internet of Things (IoT) will proliferate, and IoT devices will be included in many 5G application contexts, including the Smart Grid. Even though 5G technology has been designed by taking security into account, design provisions may be undermined by software-rooted vulnerabilities in IoT devices that allow threat actors to compromise the devices, demote confidentiality, integrity and availability, and even pose risks for the operation of the power grid critical infrastructures. In this paper, we assess the current state of the vulnerabilities in IoT software utilized in smart grid applications from a source code point of view. To that end, we identified and analyzed open-source software that is used in the power grid and the IoT domain that varies in characteristics and functionality, ranging from operating systems to communication protocols, allowing us to obtain a more complete view of the vulnerability landscape. The results of this study can be used in the domain of software development, to enhance the security of produced software, as well as in the domain of automated software testing, targeting improvements to vulnerability detection mechanisms, especially with a focus on the reduction of false positives.
Christos-Minas Mathas; Costas Vassilakis; Nicholas Kolokotronis; Charilaos Zarakovitis; Michail-Alexandros Kourtis. On the Design of IoT Security: Analysis of Software Vulnerabilities for Smart Grids. Energies 2021, 14, 2818 .
AMA StyleChristos-Minas Mathas, Costas Vassilakis, Nicholas Kolokotronis, Charilaos Zarakovitis, Michail-Alexandros Kourtis. On the Design of IoT Security: Analysis of Software Vulnerabilities for Smart Grids. Energies. 2021; 14 (10):2818.
Chicago/Turabian StyleChristos-Minas Mathas; Costas Vassilakis; Nicholas Kolokotronis; Charilaos Zarakovitis; Michail-Alexandros Kourtis. 2021. "On the Design of IoT Security: Analysis of Software Vulnerabilities for Smart Grids." Energies 14, no. 10: 2818.
Collaborative filtering-based recommendation systems consider users’ likings and interests, articulated as ratings within a database to offer personalized recommendations. Unfortunately, many collaborative filtering datasets exhibit the “grey sheep” 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 “grey sheep” 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–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.
Dionisis Margaris; Dimitris Spiliotopoulos; Gregory Karagiorgos; Costas Vassilakis; Dionysios Vasilopoulos. On Addressing the Low Rating Prediction Coverage in Sparse Datasets Using Virtual Ratings. SN Computer Science 2021, 2, 1 -19.
AMA StyleDionisis Margaris, Dimitris Spiliotopoulos, Gregory Karagiorgos, Costas Vassilakis, Dionysios Vasilopoulos. On Addressing the Low Rating Prediction Coverage in Sparse Datasets Using Virtual Ratings. SN Computer Science. 2021; 2 (4):1-19.
Chicago/Turabian StyleDionisis Margaris; Dimitris Spiliotopoulos; Gregory Karagiorgos; Costas Vassilakis; Dionysios Vasilopoulos. 2021. "On Addressing the Low Rating Prediction Coverage in Sparse Datasets Using Virtual Ratings." SN Computer Science 2, no. 4: 1-19.
Collaborative filtering algorithms take into account users’ tastes and interests, expressed as ratings, in order to formulate personalized recommendations. These algorithms initially identify each user’s “near neighbors,” i.e., users having highly similar tastes and likings. Then, their already entered ratings are used, in order to formulate rating predictions, and predictions are typically used thereafter to drive the recommendation formulation process, e.g., by selecting the items with the top-K rating predictions; henceforth, the quality of the rating predictions significantly affects the quality of the generated recommendations. However, certain types of users prefer to experience (purchase, listen to, watch, play) items the moment they become available in the stores, or even preorder, while other types of users prefer to wait for a period of time before experiencing, until a satisfactory amount of feedback (reviews and/or evaluations) becomes available for the item of interest. Notably, a user may apply varying practices on different item categories, i.e., be keen to experience new items in some categories while being uneager in other categories. To formulate successful recommendations, a recommender system should align with users’ patterns of practice and avoid recommending a newly released item to users that delay to experience new items in the particular category, and vice versa. Insofar, however, no algorithm that takes into account this aspect has been proposed. In this work, we (1) present the Experiencing Period Criterion rating prediction algorithm (CFEPC) which modifies the rating prediction value based on the combination of the users’ experiencing wait period in a certain item category and the period the rating to be predicted belongs to, so as to enhance the prediction accuracy of recommender systems and (2) evaluate the accuracy of the proposed algorithm using seven widely used datasets, considering two widely employed user similarity metrics, as well as four accuracy metrics. The results show that the CFEPC algorithm, presented in this paper, achieves a considerable rating prediction quality improvement, in all the datasets tested, indicating that the CFEPC algorithm can provide a basis for formulating more successful recommendations.
Dionisis Margaris; Dimitris Spiliotopoulos; Costas Vassilakis; Dionysios Vasilopoulos. Improving collaborative filtering’s rating prediction accuracy by introducing the experiencing period criterion. Neural Computing and Applications 2020, 1 -18.
AMA StyleDionisis Margaris, Dimitris Spiliotopoulos, Costas Vassilakis, Dionysios Vasilopoulos. Improving collaborative filtering’s rating prediction accuracy by introducing the experiencing period criterion. Neural Computing and Applications. 2020; ():1-18.
Chicago/Turabian StyleDionisis Margaris; Dimitris Spiliotopoulos; Costas Vassilakis; Dionysios Vasilopoulos. 2020. "Improving collaborative filtering’s rating prediction accuracy by introducing the experiencing period criterion." Neural Computing and Applications , no. : 1-18.
Dimitris Koryzis; Fotios Fitsilis; Dimitris Spiliotopoulos; Theocharis Theocharopoulos; Dionisis Margaris; Costas Vassilakis. Policy Making Analysis and Practitioner User Experience. Structural Information and Communication Complexity 2020, 415 -431.
AMA StyleDimitris Koryzis, Fotios Fitsilis, Dimitris Spiliotopoulos, Theocharis Theocharopoulos, Dionisis Margaris, Costas Vassilakis. Policy Making Analysis and Practitioner User Experience. Structural Information and Communication Complexity. 2020; ():415-431.
Chicago/Turabian StyleDimitris Koryzis; Fotios Fitsilis; Dimitris Spiliotopoulos; Theocharis Theocharopoulos; Dionisis Margaris; Costas Vassilakis. 2020. "Policy Making Analysis and Practitioner User Experience." Structural Information and Communication Complexity , no. : 415-431.
Nowadays, due to the huge volume of information available on the web, the need for personalization is more than necessary. Choosing the right information for each user is as important as the way this information is presented to him or her. Currently, user-triggered recommendation requests for web services are implemented as an automatic recommendation based on parametric computation. This work reports on a specialized user interface for business processes, where writing code entails invocation of business process information. The paper presents the user interface design for Personalized Web Service Selection in Business Process scenario execution and the user evaluation by business process engineers.
Dionisis Margaris; Dimitris Spiliotopoulos; Costas Vassilakis; Gregory Karagiorgos. A User Interface for Personalized Web Service Selection in Business Processes. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 560 -573.
AMA StyleDionisis Margaris, Dimitris Spiliotopoulos, Costas Vassilakis, Gregory Karagiorgos. A User Interface for Personalized Web Service Selection in Business Processes. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():560-573.
Chicago/Turabian StyleDionisis Margaris; Dimitris Spiliotopoulos; Costas Vassilakis; Gregory Karagiorgos. 2020. "A User Interface for Personalized Web Service Selection in Business Processes." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 560-573.
This work presents the universal access design principles and methods for natural language communication design in e-learning for the disabled. It unfolds a theoretical perspective to the design-for-all methodology and provides a framework description for technologies for creating accessible content for educational content communication. Main concerns include the problem identification of design issues for universal accessibility of spoken material, the primary pedagogical aspects that such content implementation should follow upon, as well as look into the state of the most popular e-learning platforms for which educators create and communicate educational content in an e-learning environment. References to massive open online course platform types of content that exist at the moment are examined in order to understand the challenges of bridging the gap between the modern design of rich courses and universal accessibility. The paper looks into the existing technologies for accessibility and a frame for analysis, including methodological and design issues, available resources and implementation using the existing technologies for accessibility and the perception of the designer as well as the user standpoint. Finally, a study to inform and access how potential educators may perceive the accessibility factor shows that accessible content is a major requirement toward a successful path to universally accessible e-learning.
Dimitris Spiliotopoulos; Vassilis Poulopoulos; Dionisis Margaris; Eleni Makri; Costas Vassilakis. MOOC Accessibility from the Educator Perspective. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 114 -125.
AMA StyleDimitris Spiliotopoulos, Vassilis Poulopoulos, Dionisis Margaris, Eleni Makri, Costas Vassilakis. MOOC Accessibility from the Educator Perspective. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():114-125.
Chicago/Turabian StyleDimitris Spiliotopoulos; Vassilis Poulopoulos; Dionisis Margaris; Eleni Makri; Costas Vassilakis. 2020. "MOOC Accessibility from the Educator Perspective." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 114-125.
User experience design and subsequent usability evaluation can benefit from knowledge about user interaction, types, deployment settings and situations. Most of the time, the user type and generic requirements are given or can be obtained and used to model interaction during the design phase. The deployment settings and situations can be collected through the needfinding phase, either via user feedback or via the automatic analysis of existing data. Personas may be defined using the aforementioned information through user research analysis or data analysis. This work utilizes an approach to activate an accurate persona definition early in the design cycle, using topic detection to semantically enrich the data that are used to derive the persona details. This work uses Twitter data from a music event to extract information that can be used to assist persona creation. A user study in persona construction compares the topic modelling metadata to a traditional user collected data analysis for persona construction. The results show that the topic information-driven constructed personas are perceived as having better clarity, completeness and credibility. Additionally, the human users feel more attracted and similar to such personas. This work may be used to model personas and recommend suitable ones to designers of other products, such as advertisers, game designers and moviegoers.
Dimitris Spiliotopoulos; Dionisis Margaris; Costas Vassilakis. Data-Assisted Persona Construction Using Social Media Data. Big Data and Cognitive Computing 2020, 4, 21 .
AMA StyleDimitris Spiliotopoulos, Dionisis Margaris, Costas Vassilakis. Data-Assisted Persona Construction Using Social Media Data. Big Data and Cognitive Computing. 2020; 4 (3):21.
Chicago/Turabian StyleDimitris Spiliotopoulos; Dionisis Margaris; Costas Vassilakis. 2020. "Data-Assisted Persona Construction Using Social Media Data." Big Data and Cognitive Computing 4, no. 3: 21.
Metacognitive training reflects knowledge, consideration and control over decision-making and task performance evident in any social and learning context. Interest in understanding the best account of effective (win-win) negotiation emerges in different social and cultural interactions worldwide. The research presented in this paper explores an extended study of metacognitive training system during negotiation using an embodied conversational agent. It elaborates on the findings from the usability evaluation employing 40 adult learners pre- and postinteraction with the system, reporting on the usability and metacognitive, individual- and community-level related attributes. Empirical evidence indicates (a) higher levels of self-efficacy, individual readiness to change and civic action after user-system experience, (b) significant and positive direct associations between self-efficacy, self-regulation, interpersonal and problem-solving skills, individual readiness to change, mastery goal orientation and civic action pre- and postinteraction and (c) gender differences in the perceptions of system usability performance according to country of origin. Theoretical and practical implications in tandem with future research avenues are discussed in light of embodied conversational agent metacognitive training in negotiation.
Dimitris Spiliotopoulos; Eleni Makri; Costas Vassilakis; Dionisis Margaris. Multimodal Interaction: Correlates of Learners’ Metacognitive Skill Training Negotiation Experience. Information 2020, 11, 381 .
AMA StyleDimitris Spiliotopoulos, Eleni Makri, Costas Vassilakis, Dionisis Margaris. Multimodal Interaction: Correlates of Learners’ Metacognitive Skill Training Negotiation Experience. Information. 2020; 11 (8):381.
Chicago/Turabian StyleDimitris Spiliotopoulos; Eleni Makri; Costas Vassilakis; Dionisis Margaris. 2020. "Multimodal Interaction: Correlates of Learners’ Metacognitive Skill Training Negotiation Experience." Information 11, no. 8: 381.
Collaborative filtering algorithms formulate personalized recommendations for a user, first by analysing already entered ratings to identify other users with similar tastes to the user (termed as near neighbours), and then using the opinions of the near neighbours to predict which items the target user would like. However, in sparse datasets, too few near neighbours can be identified, resulting in low accuracy predictions and even a total inability to formulate personalized predictions. This paper addresses the sparsity problem by presenting an algorithm that uses robust predictions, that is predictions deemed as highly probable to be accurate, as derived ratings. Thus, the density of sparse datasets increases, and improved rating prediction coverage and accuracy are achieved. The proposed algorithm, termed as CFDR, is extensively evaluated using (1) seven widely-used collaborative filtering datasets, (2) the two most widely-used correlation metrics in collaborative filtering research, namely the Pearson correlation coefficient and the cosine similarity, and (3) the two most widely-used error metrics in collaborative filtering, namely the mean absolute error and the root mean square error. The evaluation results show that, by successfully increasing the density of the datasets, the capacity of collaborative filtering systems to formulate personalized and accurate recommendations is considerably improved.
Dionisis Margaris; Dimitris Spiliotopoulos; Gregory Karagiorgos; Costas Vassilakis. An Algorithm for Density Enrichment of Sparse Collaborative Filtering Datasets Using Robust Predictions as Derived Ratings. Algorithms 2020, 13, 174 .
AMA StyleDionisis Margaris, Dimitris Spiliotopoulos, Gregory Karagiorgos, Costas Vassilakis. An Algorithm for Density Enrichment of Sparse Collaborative Filtering Datasets Using Robust Predictions as Derived Ratings. Algorithms. 2020; 13 (7):174.
Chicago/Turabian StyleDionisis Margaris; Dimitris Spiliotopoulos; Gregory Karagiorgos; Costas Vassilakis. 2020. "An Algorithm for Density Enrichment of Sparse Collaborative Filtering Datasets Using Robust Predictions as Derived Ratings." Algorithms 13, no. 7: 174.
The paper presents recent work on the design and development of AI chatbots for museums using Knowledge Graphs (KGs). The utilization of KGs as a key technology for implementing chatbots raises not only issues related to the representation and structuring of exhibits’ knowledge in suitable formalism and models, but also issues related to the translation of natural language dialogues to and from the selected technology for the formal representation and structuring of information and knowledge. Moreover, such a translation must be as transparent as possible to visitors, towards a realistic human-like question-answering process. The paper reviews and evaluates a number of recent approaches for the use of KGs in developing AI chatbots, as well as key tools that provide solutions for natural language translation and the querying of Knowledge Bases and Linked Open Data sources. This evaluation aims to provide answers to issues that are identified within the proposed MuBot approach for designing and implementing AI chatbots for museums. The paper also presents Cretan MuBot, the first experimental KG/Ontology-based AI chatbot of the MuBot Platform, which is under development in the Heracleum Archaeological Museum.
Savvas Varitimiadis; Konstantinos Kotis; Dimitris Spiliotopoulos; Costas Vassilakis; Dionisis Margaris. “Talking” Triples to Museum Chatbots. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 281 -299.
AMA StyleSavvas Varitimiadis, Konstantinos Kotis, Dimitris Spiliotopoulos, Costas Vassilakis, Dionisis Margaris. “Talking” Triples to Museum Chatbots. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():281-299.
Chicago/Turabian StyleSavvas Varitimiadis; Konstantinos Kotis; Dimitris Spiliotopoulos; Costas Vassilakis; Dionisis Margaris. 2020. "“Talking” Triples to Museum Chatbots." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 281-299.
This work addresses the challenges of creating usable and personalized conversational interfaces for broad, yet applicable, domains that require user engagement and learning, such as museum chatbots. Whether the chatbots are standalone or coupled with virtual agents or real-life robots, the functional requirements for interaction that targets specific learning aspects would be expected to be more or less similar. This work reports on experimental semantics-driven conversational interface design for chatbots in museum settings, targeting visitors to converse about exhibits and learn information about their style, the artists, the era, and other aspects related to them. Depending on the semantics (presentation, learning, exploration), chatbot scenarios were designed and evaluated by participants in a formative evaluation. The evaluation show that user requirement perception manifests in expectations on the semantic level, instead of just the technical level. The results between the scenarios are compared to see how the semantics considered for the design transferred to the implementation and to the user perception.
Dimitris Spiliotopoulos; Konstantinos Kotis; Costas Vassilakis; Dionisis Margaris. Semantics-Driven Conversational Interfaces for Museum Chatbots. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 255 -266.
AMA StyleDimitris Spiliotopoulos, Konstantinos Kotis, Costas Vassilakis, Dionisis Margaris. Semantics-Driven Conversational Interfaces for Museum Chatbots. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():255-266.
Chicago/Turabian StyleDimitris Spiliotopoulos; Konstantinos Kotis; Costas Vassilakis; Dionisis Margaris. 2020. "Semantics-Driven Conversational Interfaces for Museum Chatbots." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 255-266.
The way that users provide feedback on items regarding their satisfaction varies among systems: in some systems, only explicit ratings can be entered; in other systems textual reviews are accepted; and in some systems, both feedback types are accommodated. Recommender systems can readily exploit explicit ratings in the rating prediction and recommendation formulation process, however textual reviews -which in the context of many social networks are in abundance and significantly outnumber numeric ratings- need to be converted to numeric ratings. While numerous approaches exist that calculate a user's rating based on the respective textual review, all such approaches may introduce errors, in the sense that the process of rating calculation based on textual reviews involves an uncertainty level, due to the characteristics of the human language, and therefore the calculated ratings may not accurately reflect the actual ratings that the corresponding user would enter. In this work (1) we examine the features of textual reviews, which affect the reliability of the review-to-rating conversion procedure, (2) we compute a confidence level for each rating, which reflects the uncertainty level for each conversion process, (3) we exploit this metric both in the users’ similarity computation and in the prediction formulation phases in recommender systems, by presenting a novel rating prediction algorithm and (4) we validate the accuracy of the presented algorithm in terms of (i) rating prediction accuracy, using widely-used recommender systems datasets and (ii) recommendations generated for social network user satisfaction and precision, where textual reviews are abundant.
Dionisis Margaris; Costas Vassilakis; Dimitris Spiliotopoulos. What makes a review a reliable rating in recommender systems? Information Processing & Management 2020, 57, 102304 .
AMA StyleDionisis Margaris, Costas Vassilakis, Dimitris Spiliotopoulos. What makes a review a reliable rating in recommender systems? Information Processing & Management. 2020; 57 (6):102304.
Chicago/Turabian StyleDionisis Margaris; Costas Vassilakis; Dimitris Spiliotopoulos. 2020. "What makes a review a reliable rating in recommender systems?" Information Processing & Management 57, no. 6: 102304.
Advancements in cultural informatics have significantly influenced the way we perceive, analyze, communicate and understand culture. New data sources, such as social media, digitized cultural content, and Internet of Things (IoT) devices, have allowed us to enrich and customize the cultural experience, but at the same time have created an avalanche of new data that needs to be stored and appropriately managed in order to be of value. Although data management plays a central role in driving forward the cultural heritage domain, the solutions applied so far are fragmented, physically distributed, require specialized IT knowledge to deploy, and entail significant IT experience to operate even for trivial tasks. In this work, we present Hydria, an online data lake that allows users without any IT background to harvest, store, organize, analyze and share heterogeneous, multi-faceted cultural heritage data. Hydria provides a zero-administration, zero-cost, integrated framework that enables researchers, museum curators and other stakeholders within the cultural heritage domain to easily (i) deploy data acquisition services (like social media scrapers, focused web crawlers, dataset imports, questionnaire forms), (ii) design and manage versatile customizable data stores, (iii) share whole datasets or horizontal/vertical data shards with other stakeholders, (iv) search, filter and analyze data via an expressive yet simple-to-use graphical query engine and visualization tools, and (v) perform user management and access control operations on the stored data. To the best of our knowledge, this is the first solution in the literature that focuses on collecting, managing, analyzing, and sharing diverse, multi-faceted data in the cultural heritage domain and targets users without an IT background.
Kimon Deligiannis; Paraskevi Raftopoulou; Christos Tryfonopoulos; Nikos Platis; Costas Vassilakis. Hydria: An Online Data Lake for Multi-Faceted Analytics in the Cultural Heritage Domain. Big Data and Cognitive Computing 2020, 4, 7 .
AMA StyleKimon Deligiannis, Paraskevi Raftopoulou, Christos Tryfonopoulos, Nikos Platis, Costas Vassilakis. Hydria: An Online Data Lake for Multi-Faceted Analytics in the Cultural Heritage Domain. Big Data and Cognitive Computing. 2020; 4 (2):7.
Chicago/Turabian StyleKimon Deligiannis; Paraskevi Raftopoulou; Christos Tryfonopoulos; Nikos Platis; Costas Vassilakis. 2020. "Hydria: An Online Data Lake for Multi-Faceted Analytics in the Cultural Heritage Domain." Big Data and Cognitive Computing 4, no. 2: 7.
This paper presents SemMR, a semantic framework for modelling interactions between human and non-human entities and managing reusable and optimized cultural experiences, towards a shared cultural experience ecosystem that might seamlessly accommodate mixed reality experiences. The SemMR framework synthesizes and integrates interaction data into semantically rich reusable structures and facilitates the interaction between different types of entities in a symbiotic way, within a large, virtual, and fully experiential open world, promoting experience sharing at the user level, as well as data/application interoperability and low-effort implementation at the software engineering level. The proposed semantic framework introduces methods for low-effort implementation and the deployment of open and reusable cultural content, applications, and tools, around the concept of cultural experience as a semantic trajectory or simply, experience as a trajectory (eX-trajectory). The methods facilitate the collection and analysis of data regarding the behaviour of users and their interaction with other users and the environment, towards optimizing eX-trajectories via reconfiguration. The SemMR framework supports the synthesis, enhancement, and recommendation of highly complex reconfigurable eX-trajectories, while using semantically integrated disparate and heterogeneous related data. Overall, this work aims to semantically manage interactions and experiences through the eX-trajectory concept, towards delivering enriched cultural experiences.
Costas Vassilakis; Konstantinos Kotis; Dimitris Spiliotopoulos; Dionisis Margaris; Vlasios Kasapakis; Christos-Nikolaos Anagnostopoulos; Georgios Santipantakis; George A. Vouros; Theodore Kotsilieris; Volha Petukhova; Andrei Malchanau; Ioanna Lykourentzou; Kaj Michael Helin; Artem Revenko; Nenad Gligoric; Boris Pokric. A Semantic Mixed Reality Framework for Shared Cultural Experiences Ecosystems. Big Data and Cognitive Computing 2020, 4, 6 .
AMA StyleCostas Vassilakis, Konstantinos Kotis, Dimitris Spiliotopoulos, Dionisis Margaris, Vlasios Kasapakis, Christos-Nikolaos Anagnostopoulos, Georgios Santipantakis, George A. Vouros, Theodore Kotsilieris, Volha Petukhova, Andrei Malchanau, Ioanna Lykourentzou, Kaj Michael Helin, Artem Revenko, Nenad Gligoric, Boris Pokric. A Semantic Mixed Reality Framework for Shared Cultural Experiences Ecosystems. Big Data and Cognitive Computing. 2020; 4 (2):6.
Chicago/Turabian StyleCostas Vassilakis; Konstantinos Kotis; Dimitris Spiliotopoulos; Dionisis Margaris; Vlasios Kasapakis; Christos-Nikolaos Anagnostopoulos; Georgios Santipantakis; George A. Vouros; Theodore Kotsilieris; Volha Petukhova; Andrei Malchanau; Ioanna Lykourentzou; Kaj Michael Helin; Artem Revenko; Nenad Gligoric; Boris Pokric. 2020. "A Semantic Mixed Reality Framework for Shared Cultural Experiences Ecosystems." Big Data and Cognitive Computing 4, no. 2: 6.
When information from traditional recommender systems is augmented with information about user relationships that social networks store, more successful recommendations can be produced. However, this information regarding user relationships may not always be available, since some users may not consent to the use of their social network information for recommendations or may not have social network accounts at all. Moreover, the rating data (categories and characteristics of products) may be unavailable for a recommender system. In this paper, we present an algorithm that can be applied in any social network-aware recommender system that utilizes the users’ ratings on items and users’ social relations. The proposed algorithm addresses the issues of limited social network information or limited collaborative filtering information for some users by adapting its behavior, taking into account the density and utility of each user’s social network and collaborative filtering neighborhoods. Through this adaptation, the proposed algorithm achieves considerable improvement in rating prediction accuracy. Furthermore, the proposed algorithm can be easily implemented in recommender systems.
Dionisis Margaris; Anna Kobusinska; Dimitris Spiliotopoulos; Costas Vassilakis. An Adaptive Social Network-Aware Collaborative Filtering Algorithm for Improved Rating Prediction Accuracy. IEEE Access 2020, 8, 68301 -68310.
AMA StyleDionisis Margaris, Anna Kobusinska, Dimitris Spiliotopoulos, Costas Vassilakis. An Adaptive Social Network-Aware Collaborative Filtering Algorithm for Improved Rating Prediction Accuracy. IEEE Access. 2020; 8 (99):68301-68310.
Chicago/Turabian StyleDionisis Margaris; Anna Kobusinska; Dimitris Spiliotopoulos; Costas Vassilakis. 2020. "An Adaptive Social Network-Aware Collaborative Filtering Algorithm for Improved Rating Prediction Accuracy." IEEE Access 8, no. 99: 68301-68310.
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 is an extensive study of multimodal application interaction (virtual agent, spoken dialogue, visual communication of progress) for metacognitive skill training via negotiation skill training scenarios. 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.
Eleni Makri; Dimitris Spiliotopoulos; Costas Vassilakis; Dionisis Margaris. Human behaviour in multimodal interaction: main effects of civic action and interpersonal and problem-solving skills. Journal of Ambient Intelligence and Humanized Computing 2020, 11, 5991 -6006.
AMA StyleEleni Makri, Dimitris Spiliotopoulos, Costas Vassilakis, Dionisis Margaris. Human behaviour in multimodal interaction: main effects of civic action and interpersonal and problem-solving skills. Journal of Ambient Intelligence and Humanized Computing. 2020; 11 (12):5991-6006.
Chicago/Turabian StyleEleni Makri; Dimitris Spiliotopoulos; Costas Vassilakis; Dionisis Margaris. 2020. "Human behaviour in multimodal interaction: main effects of civic action and interpersonal and problem-solving skills." Journal of Ambient Intelligence and Humanized Computing 11, no. 12: 5991-6006.
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’ 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.
Dionisis Margaris; Costas Vassilakis; Dimitris Spiliotopoulos. Handling uncertainty in social media textual information for improving venue recommendation formulation quality in social networks. Social Network Analysis and Mining 2019, 9, 64 .
AMA StyleDionisis Margaris, Costas Vassilakis, Dimitris Spiliotopoulos. Handling uncertainty in social media textual information for improving venue recommendation formulation quality in social networks. Social Network Analysis and Mining. 2019; 9 (1):64.
Chicago/Turabian StyleDionisis Margaris; Costas Vassilakis; Dimitris Spiliotopoulos. 2019. "Handling uncertainty in social media textual information for improving venue recommendation formulation quality in social networks." Social Network Analysis and Mining 9, no. 1: 64.
Intermittent faults are a very common problem in the software world, while difficult to be debugged. Most of the existing approaches though 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. 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 cause of intermittent faults. It is the responsibility of the user to accept or reject these proposals. An advantage of this method is that it removes the need for having predefined assertion points in the code, being able to detect potential sources of intermittent faults in the whole bulk of the code, with no instrumentation requirements on the side of the programmer. The proposed approach exploits information from the dynamic behavior of the program. In comparison with parser-based approaches which analyze only the program structure, our approach is immutable to language term changes and in general is not depending on any user-provided assertions or configuration.
Panagiotis Sotiropolos; Costas Vassilakis. Detection of intermittent faults in software programs through identification of suspicious shared variable access patterns. Journal of Systems and Software 2019, 159, 110455 .
AMA StylePanagiotis Sotiropolos, Costas Vassilakis. Detection of intermittent faults in software programs through identification of suspicious shared variable access patterns. Journal of Systems and Software. 2019; 159 ():110455.
Chicago/Turabian StylePanagiotis Sotiropolos; Costas Vassilakis. 2019. "Detection of intermittent faults in software programs through identification of suspicious shared variable access patterns." Journal of Systems and Software 159, no. : 110455.