This page has only limited features, please log in for full access.
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.
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.
Every day, many people use at least one social network (or social media) account. This development has been boosted by the rapid growth of technology, making both smartphones and mobile data much more accessible and inexpensive. Therefore, the number of social networks users is growing rapidly, accounting more than 1 billion active users worldwide. The ease of use, as well as the ability to communicate without spatial and temporal restrictions underpinned the rapid increase of the popularity of social networks, as well as their wide acceptance by the general public. This popularity influences people's opinion on many issues, shapes consumer habits and behaviour, mood, etc. The work of many scientists across multiple disciplines has focused on studying social media from various perspectives, including marketing, journalism and sociology. This paper investigates how trending information from social media can be used to match topics of interest from cultural database indices. Matches identified in this process are then presented to cultural venue curators, who can then review matches, mark them as useful or reject them, and exploit them for various tasks, and most notably for the promotion of the venue and its content. More specifically, we have developed an application, which collects the 10 most popular twitter trends and then matches their content with the contents of a given cultural database. Using the results of this match, items from the database that may be related to current issues may be recommended to the user. As a result, these matches, after being inspected and approved by the administrator, can be used to attract the interest of the target audience, highlighting the correlation of current issues with the database's items.
Costas Vassilakis; Dimitra Maniataki; George Lepouras; Angeliki Antoniou; Dimitris Spiliotopoulos; Vassilis Poulopoulos; Manolis Wallace; Dionisis Margaris. Database Knowledge Enrichment Utilizing Trending Topics from Twitter. 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) 2020, 870 -876.
AMA StyleCostas Vassilakis, Dimitra Maniataki, George Lepouras, Angeliki Antoniou, Dimitris Spiliotopoulos, Vassilis Poulopoulos, Manolis Wallace, Dionisis Margaris. Database Knowledge Enrichment Utilizing Trending Topics from Twitter. 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). 2020; ():870-876.
Chicago/Turabian StyleCostas Vassilakis; Dimitra Maniataki; George Lepouras; Angeliki Antoniou; Dimitris Spiliotopoulos; Vassilis Poulopoulos; Manolis Wallace; Dionisis Margaris. 2020. "Database Knowledge Enrichment Utilizing Trending Topics from Twitter." 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) , no. : 870-876.
Businesses benefit by recommender systems since the latter analyse reviews and ratings of products and services, providing useful insight of the buyer perception of them. One of the most popular, successful and easy-to-build recommender system techniques is collaborative filtering. Recommender systems take into account social network information, to achieve more accurate predictions. Unfortunately, however, many applications do not have full access to such “rich” information, so they have to properly manage the limited information, which, in the worst case, is comprised of just the user relationships in the social network. A social network collaborative filtering system combines the two sources of information, in order to formulate rating predictions which will lead to recommendations. However, the vast majority of users change their tastes, as time goes by, a phenomenon termed as concept drift, and in order for a recommender system to be successful, it must effectively face this problem. In this paper, we present a social network collaborative filtering rating prediction algorithm that tunes the weight-importance of each source of information based on the age of the information. The proposed algorithm considerably improves rating prediction accuracy, while it can be easily integrated in social network collaborative filtering recommender systems.
Dionisis Margaris; Dimitris Spiliotopoulos; Costas Vassilakis. Neighbourhood Aging Factors for Limited Information Social Network Collaborative Filtering. 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) 2020, 877 -883.
AMA StyleDionisis Margaris, Dimitris Spiliotopoulos, Costas Vassilakis. Neighbourhood Aging Factors for Limited Information Social Network Collaborative Filtering. 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). 2020; ():877-883.
Chicago/Turabian StyleDionisis Margaris; Dimitris Spiliotopoulos; Costas Vassilakis. 2020. "Neighbourhood Aging Factors for Limited Information Social Network Collaborative Filtering." 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) , no. : 877-883.
Terrorism is a major disincentive to tourism. It affects both a country or area's tourists as well as local residents and staff. On the one hand, the prospective tourist is likely to avoid traveling to a high-risk country due to safety concerns, and thus lose the opportunity to visit it, while, on the other hand, the tourism of the country would decline. This work solves the above-mentioned problem by (1) showing that reasonably safe visits to high-risk countries can be predicted with high precision, using limited information, including data on attacks and fatalities from recent years, which is widely available, and (2) creating an algorithm that recommends these periods to potential travellers. The findings of this work would be useful for tourists, citizens, businesses and operators, as well as related stakeholders.
Dimitris Spiliotopoulos; Costas Vassilakis; Dionisis Margaris. On Recommending Safe Travel Periods to High Attack Risk Destinations. 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) 2020, 854 -861.
AMA StyleDimitris Spiliotopoulos, Costas Vassilakis, Dionisis Margaris. On Recommending Safe Travel Periods to High Attack Risk Destinations. 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). 2020; ():854-861.
Chicago/Turabian StyleDimitris Spiliotopoulos; Costas Vassilakis; Dionisis Margaris. 2020. "On Recommending Safe Travel Periods to High Attack Risk Destinations." 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) , no. : 854-861.
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.
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 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.
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.
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.
Costas Vassilakis; Dionisis Margaris. Improving collaborative filtering's rating prediction coverage in sparse datasets by exploiting the 'friend of a friend' concept. International Journal of Big Data Intelligence 2020, 7, 47 .
AMA StyleCostas Vassilakis, Dionisis Margaris. Improving collaborative filtering's rating prediction coverage in sparse datasets by exploiting the 'friend of a friend' concept. International Journal of Big Data Intelligence. 2020; 7 (1):47.
Chicago/Turabian StyleCostas Vassilakis; Dionisis Margaris. 2020. "Improving collaborative filtering's rating prediction coverage in sparse datasets by exploiting the 'friend of a friend' concept." International Journal of Big Data Intelligence 7, no. 1: 47.
In this work we present an integer programming-based algorithm for adapting the execution of WS-BPEL scenarios, through the dynamic selection of the services to be invoked, according to criteria and policies set by the user. The proposed algorithm is experimentally evaluated both in terms of adaptation quality and adaptation computation overhead. The experimental results demonstrate that the proposed approach achieves to considerably improve adaptation speed, as compared to the exhaustive search algorithm which is considered as a baseline, while at the same time maintaining adaptation quality.
Dionisis Margaris; Dimitris Spiliotopoulos; Apostolos Kardiasmenos; Dimitrios Pantazopoulos. An integer programming-based algorithm for optimising the WS-BPEL scenario execution adaptation process. International Journal of Web Engineering and Technology 2020, 15, 307 .
AMA StyleDionisis Margaris, Dimitris Spiliotopoulos, Apostolos Kardiasmenos, Dimitrios Pantazopoulos. An integer programming-based algorithm for optimising the WS-BPEL scenario execution adaptation process. International Journal of Web Engineering and Technology. 2020; 15 (3):307.
Chicago/Turabian StyleDionisis Margaris; Dimitris Spiliotopoulos; Apostolos Kardiasmenos; Dimitrios Pantazopoulos. 2020. "An integer programming-based algorithm for optimising the WS-BPEL scenario execution adaptation process." International Journal of Web Engineering and Technology 15, no. 3: 307.