@article {868, title = {A Large-Scale Empirical Study of LLM Orchestration and Ensemble Strategies for Sentiment Analysis in Recommender Systems}, journal = {Future Internet}, volume = {18}, year = {2026}, pages = {112}, abstract = {

This paper presents a comprehensive empirical evaluation comparing meta-model aggregation strategies with traditional ensemble methods and standalone models for sentiment analysis in recommender systems beyond standalone large language model (LLM) performance. We investigate whether aggregating multiple LLMs through a reasoning-based meta-model provides measurable performance advantages over individual models and standard statistical aggregation approaches in zero-shot sentiment classification. Using a balanced dataset of 5000 verified Amazon purchase reviews (1000 reviews per rating category from 1 to 5 stars, sampled via two-stage stratified sampling across five product categories), we evaluate 12 different leading pre-trained LLMs from four major providers (OpenAI, Anthropic, Google, and DeepSeek) in both standalone and meta-model configurations. Our experimental design systematically compares individual model performance against GPT-based meta-model aggregation and traditional ensemble baselines (majority voting, mean aggregation). Results show statistically significant improvements (McNemar{\textquoteright}s test, p \< 0.001): the GPT-5 meta-model achieves 71.40\% accuracy (10.15 percentage point improvement over the 61.25\% individual model average), while the GPT-5 mini meta-model reaches 70.32\% (9.07 percentage point improvement). These observed improvements surpass traditional ensemble methods (majority voting: 62.64\%; mean aggregation: 62.96\%), suggesting potential value in meta-model aggregation for sentiment analysis tasks. Our analysis reveals empirical patterns including neutral sentiment classification challenges (3-star ratings show 64.83\% failure rates across models), model influence hierarchies, and cost-accuracy trade-offs ($130.45 aggregation cost vs. $0.24{\textendash}$43.97 for individual models per 5000 predictions). This work provides evidence-based insights into the comparative effectiveness of LLM aggregation strategies in recommender systems, demonstrating that meta-model aggregation with natural language reasoning capabilities achieves measurable performance gains beyond statistical aggregation alone.

}, keywords = {empirical evaluation, ensemble learning, large language models, LLM orchestration, meta-model aggregation, multi-model aggregation, reasoning models, Recommender Systems, sentiment classification, zero-shot sentiment analysis}, issn = {1999-5903}, doi = {https://doi.org/10.3390/fi18020112}, url = {https://www.mdpi.com/1999-5903/18/2/112}, author = {Roumeliotis, Konstantinos I. and Margaris, Dionisis and Spiliotopoulos, Dimitris and Vassilakis, Costas} } @article {869, title = {Optimizing Collaborative Filtering for Accurate Rating Predictions in Very Sparse Datasets}, journal = {Future Internet}, volume = {18}, year = {2026}, pages = {114}, abstract = {

Collaborative filtering is one of the most widely used methods for user rating prediction in recommender systems. To evaluate a collaborative filtering system, rating datasets are typically used, which comprise thousands to millions of records consisting of user{\textendash}item{\textendash}rating tuples. Initially, a similarity metric is used to quantify the closeness between each user and every other user in the dataset, typically based on the ratings that each pair of users has given to the same items. Subsequently, the K users having the largest similarity to the target user are used to produce rating predictions, which lead to recommendations. A particularly challenging case arises when the rating dataset is very sparse. In this scenario, it is difficult not only to find users with commonly rated items but also to determine the optimal similarity metric and suitable values for variable K. Setting a small value for K results in extremely low prediction coverage, leading to unsuccessful recommendations, while setting a very large K value increases memory requirements and prediction/recommendation generation time. Through a multiparameter experiment, this work aims to determine the optimal settings for rating predictions when very sparse datasets are used in collaborative filtering recommender systems.

}, keywords = {collaborative filtering, K-nearest neighbors (KNN), optimal prediction settings, prediction accuracy, Rating prediction, Recommender Systems, similarity metrics, sparse collaborative filtering, Sparse Datasets}, issn = {1999-5903}, doi = {10.3390/fi18020114}, url = {https://www.mdpi.com/1999-5903/18/2/114}, author = {Lapadaki, Sofia-Anna and Nanos, John and Margaris, Dionisis and Vassilakis, Costas and Spiliotopoulos, Dimitris} } @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 Costas Vassilakis and Dionisis Margaris and Kotis, Konstantinos and Spiliotopoulos, Dimitris} } @article {860, title = {An evaluation review of user similarity metrics in sparse collaborative filtering datasets}, journal = {International Journal of Data Science and Analytics}, year = {2025}, abstract = {

Collaborative filtering (CF) is one of the most prominent recommender system (RecSys) techniques of the recent years. CF generates rating predictions for the items that the user has not evaluated yet, using the evaluations of users with similar likings to the same items. Therefore, in CF the task of finding these users (which can be considered as reliable recommenders) is of high importance, while this task is especially challenging on sparse datasets. To this end, many user similarity metrics have been introduced and used in the literature, such as the Vector (or Cosine) Similarity metric, the Spearman rank correlation, the Pearson Correlation Coefficient (PCC), and others. For a CF RecSys, the use of the most efficient similarity metric is of great importance. This paper assesses the effectiveness of 15 user similarity metrics in sparse CF datasets, by conducting an extensive set of experiments. These experiments include 10 sparse CF datasets with diverse item domains, two neighbour selection approaches, two rating prediction formulas, and three rating prediction accuracy metrics. The evaluation results show that the metrics that achieve the best prediction results are found to be the Spearman rank correlation, followed by the Adjusted Rand Index, the Constrained PCC, and the Chebysev distance. Interestingly, the most widely used similarity metrics in CF research, i.e. the PCC and the Cosine Similarity, are not among the best performing metrics.

}, keywords = {collaborative filtering, Evaluation review, Recommender Systems, Sparse Datasets, User similarity metrics}, issn = {2364-4168}, doi = {10.1007/s41060-025-00846-4}, author = {Sgardelis, Kiriakos and Margaris, Dionisis and Spiliotopoulos, Dimitris and Vassilakis, Costas} } @booklet {846, title = {Experimental results for Evaluating User Similarity Metrics in Sparse Collaborative Filtering Datasets (version 2)}, year = {2025}, abstract = {

In this technical report, we present the experimental findings from evaluating 15 user similarity metrics when applied in sparse collaborative filtering (CF) datasets.

}, keywords = {collaborative filtering, Evaluation, Sparse Datasets, User similarity metrics}, url = {https://soda.dit.uop.gr/sites/soda.dit.uop.gr/files/publications/technical-reports/soda-TR-24001-v2.pdf}, author = {Kiriakos Sgardelis and Dionisis Margaris and Dimitris Spiliotopoulos and Costas Vassilakis} } @article {852, title = {From Rating Predictions to Reliable Recommendations in Collaborative Filtering: The Concept of Recommendation Reliability Classes}, journal = {Big Data and Cognitive Computing}, volume = {9}, year = {2025}, pages = {106}, abstract = {

Recommender systems aspire to provide users with recommendations that have a high probability to be accepted. This is accomplished by producing rating predictions for products that they have not evaluated and afterwards, the products having the highest prediction scores are recommended to them. Collaborative filtering is a popular recommender system technique which generates rating prediction scores by blending the ratings that users with similar likings have previously set to these products. However, predictions may entail errors, which will either lead to recommending products that the users would not accept or failing to recommend products that the users would really accept. The first case is considered much more critical, since the recommender system will significantly lose reliability and consequently interest. In this paper, after performing a study on rating prediction confidence factors in collaborative filtering (a) we introduce the concept of prediction reliability classes, (b) we rank these classes in relation to the utility of the rating predictions belonging to each class and (c) we present a collaborative filtering recommendation algorithm which exploits these reliability classes for prediction formulation. The efficacy of the presented algorithm is evaluated through an extensive multi-parameter evaluation process, which demonstrates that it significantly enhances recommendation quality.

}, keywords = {collaborative filtering, Evaluation, Rating Predictions, Recommender Systems, reliability classes, reliable recommendations}, issn = {2504-2289}, doi = {10.3390/bdcc9040106}, author = {Dionisis Margaris and Costas Vassilakis and Spiliotopoulos, Dimitris} } @conference {858, title = {PROVATO: A Data-Centric Edge-AI Architecture forPrecision Livestock Monitoring Using IoT}, booktitle = {Hellenic Data Management Symposium (HDMS)}, year = {2025}, abstract = {

This work presents PROVATO (PRecisiOn liVestock monitoring with AI and IoT tOols), an edge AI and IoTpowered platform for real-time livestock monitoring. The system enables farmers to track animal health and behaviour, ensuring scalable data processing and AI inference at both the edge and the cloud. This approach supports decision-making, reduces resource waste, and improves animal welfare.

}, keywords = {IoT, livestock monitoring, livestock wellfare}, author = {Andreas Doumouras and Evangelos Mitikas and Ermis Doulos and Manos Schoinoplokakis and Giannis Nikolentzos and Christos Tryfonopoulos and Spiros Skiadopoulos and Costas Vassilakis and Paraskevi Raftopoulou*} } @article {854, title = {Using Prediction Confidence Factors to Enhance Collaborative Filtering Recommendation Quality}, journal = {Technologies}, volume = {13}, year = {2025}, pages = {181}, abstract = {

Recommender systems suggest items that users are likely to accept by predicting ratings for items they have not already rated. Collaborative filtering is a widely used method that produces these predictions, based on the ratings of similar users, termed as near neighbors. However, in many cases, prediction errors occur and, therefore, the recommender system ends up either recommending unwanted products or missing out on products the user would actually desire. As a result, the quality of the recommendations that are produced is of major importance. In this paper, we introduce an advanced collaborative filtering recommendation algorithm that upgrades the quality of the recommendations that are produced by considering, along with the rating prediction value of the items computed by the plain collaborative filtering procedure, a number of confidence factors that each rating prediction fulfills. The presented algorithm maintains high recommendation coverage, and can be applied to every collaborative filtering dataset, since it is based only on the very basic information. Based on the application of the algorithm on widely used recommender systems datasets, the proposed algorithm significantly upgrades the recommendation quality, surpassing the performance of state-of-the-art research works that also consider confidence factors.

}, keywords = {Algorithm, collaborative filtering, prediction confidence factors, recommendation quality, Recommender Systems}, issn = {2227-7080}, doi = {https://doi.org/10.3390/technologies13050181}, url = {https://doi.org/10.3390/technologies13050181}, author = {Dionisis Margaris and Spiliotopoulos, Dimitris and Sgardelis, Kiriakos and Costas Vassilakis} } @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 Costas Vassilakis and Dionisis Margaris and Kotis, Konstantinos and Spiliotopoulos, Dimitris} } @conference {838, title = {Estimating MPdist with SAX and Machine Learning}, booktitle = {New Trends in Database and Information Systems (Proceedings of ADBIS 2024, Short Papers, Workshops, Doctoral Consortium and Tutorials)}, year = {2024}, publisher = {Springer Cham}, organization = {Springer Cham}, abstract = {

MPdist is a distance measure which considers two time series to be similar if they share many similar subsequences. However, computing MPdist can be slow, especially for large time series. We propose a technique for the approximate computation of MPdist that uses the SAX representation of the time series to quickly estimate the Nearest Neighbor (NN) distance of each subsequence, and then applies a Machine Learning model to correct the accuracy loss incurred. Our method is orders of magnitude faster than the exact computation of MPdist; at the same time, our best approximation computes the NN of a time series with high accuracy. A thorough evaluation of our technique is provided.

}, keywords = {Distance metric, Machine Learning, MPdist, Random Forest, SAX, Time series}, doi = {https://doi.org/10.1007/978-3-031-70421-5_32}, author = {Mihalis Tsoukalos and Pantelis Chronis and Nikos Platis and Costas Vassilakis}, editor = {Joe Tekli and Johann Gamper and Richard Chbeir and Yannis Manolopoulos and Salma Sassi and Mirjana Ivanovic and Vargas-Solar, Genoveva and Ester Zumpano} } @article {833, title = {Experimental results for Evaluating User Similarity Metrics in Sparse Collaborative Filtering Datasets}, number = {TR-24001}, year = {2024}, month = {03/2024}, institution = {SODA lab}, type = {Technical Report}, address = {Tripolis, Greece}, keywords = {collaborative filtering, Evaluation, Sparse Datasets, User similarity metrics}, issn = {SODA-TR-24001}, author = {Kiriakos Sgardelis and Dionisis Margaris and Dimitris Spiliotopoulos and Costas Vassilakis} } @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 = {Dionisis Margaris and Sgardelis, Kiriakos and Spiliotopoulos, Dimitris and Costas Vassilakis} } @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 Costas Vassilakis and Dionisis Margaris and Kotis, Konstantinos and Spiliotopoulos, Dimitris} } @conference {839, title = {Improving Recommendation Quality in Collaborative Filtering by Including Prediction Confidence Factors}, booktitle = {Proceedings of the 20th International Conference on Web Information Systems and Technologies (WEBIST 24)}, year = {2024}, publisher = {SCITEPRESS Digital Library}, organization = {SCITEPRESS Digital Library}, abstract = {

Collaborative filtering is a prevalent recommender system technique which generates rating predictions based on the rating values given by the users{\textquoteright} near neighbours. Consequently, for each user, the items scoring the highest prediction values are recommended to them. Unfortunately, predictions inherently entail errors, which, in the case of recommender systems, manifest as unsuccessful recommendations. However, along with each rating prediction value, prediction confidence factors can be computed. As a result, items having low prediction confidence factor values, can be either declined for recommendation or have their recommendation priority demoted. In the former case, some users may receive fewer recommended items or even none, especially when using a sparse dataset. In this paper, we present an algorithm that determines the items to be recommended by considering both the rating prediction values and confidence factors of predictions, allowing for predictions with higher confidence factors to outrank predictions with higher value, but lower confidence. The presented algorithm achieves to enhance the recommendation quality, while at the same time retaining the number of recommendations for each user.

}, keywords = {Algorithm, Certainty factors, collaborative filtering, Rating Predictions, Recommender Systems}, author = {Kiriakos Sgardelis and Dionisis Margaris and Dimitris Spiliotopoulos and Costas Vassilakis and Stefanos Ougiaroglou} } @conference {840, title = {Predicting Agricultural Product and Supplies Prices using Artificial Intelligence}, booktitle = {Proceedings of the 16th International Conference on Knowledge Management and Information Systems}, year = {2024}, publisher = {SCITEPRESS Digital Library}, organization = {SCITEPRESS Digital Library}, abstract = {

This work focuses on the prediction of agricultural product and supply prices using historical data and artificial intelligence methods. Agricultural product and supply prices are important for the economy and growth of agriculture. Using modern data analysis and deep learning methods, a forecasting model was developed to help us predict future price trends. The data used include the sales prices of crop products and the purchase prices of agricultural inputs. The developed forecasting methods exhibit high accuracy for predicting the actual prices of products and supplies, with error margins ranging from 0.29\% to 9.8\%, while they can also predict price rises and falls, with respective success rates ranging from 73.29\% to 84.96\%.

}, keywords = {Agricultural Products, Data Integration, deep learning, Forecasting Models, Machine Learning, Price Forecasting}, author = {Ioannis Dionissopoulos and Fotis Assimakopoulos and Dimitris Spiliotopoulos and Dionisis Margaris and Costas Vassilakis} } @article {831, title = {Blockchain meets Internet of Things (IoT) forensics: A unified framework for IoT ecosystems}, journal = {Internet of Things}, year = {2023}, month = {oct}, pages = {100968}, abstract = {

With the continuously increasing number of security incidents targeting sensors, embedded devices, and other electronics appliances and systems that comprise the so-called Internet of things (IoT), the field of IoT forensics emerged as a branch of digital forensics focusing on the investigation of attacks in the IoT ecosystem. Digital evidence authenticity, integrity, confidentiality, and privacy are among the factors affecting the investigation process in IoT forensics, which can be addressed by employing the blockchain as the means not only for the preservation of digital evidence, but also for other steps of a digital forensics investigation process. The high-level goal of the paper is three-fold: to establish a holistic IoT forensics process as a reference point against which blockchain integration patterns and best practices will be identified in order to yield a robust, widely accepted and scalable architecture of the blockchain-enabled solution; to integrate the blockchain with the proposed IoT forensics process for addressing the above challenges; and to evaluate the overall solution highlighting improvements and potential performance bottlenecks. The proposed blockchain-enabled platform leverages multi-access edge computing and has been implemented with Hyperledger Fabric on an extensive virtualized testbed providing a realistic smart home environment. A thorough evaluation was conducted with real cyber-attacks generating digital evidence at high rates for testing the platform{\textquoteright}s behavior at high load. The experimental results showed that the proposed platform achieved high throughput, excessively low latency, and zero error rate in the operation of the blockchain network.

}, keywords = {blockchain, cyber-security, Digital forensics, Edge computing, Internet of Things}, doi = {10.1016/j.iot.2023.100968}, url = {https://www.sciencedirect.com/science/article/pii/S2542660523002913}, author = {Sotirios Brotsis and Konstantinos P. Grammatikakis and Dimitrios Kavallieros and Antonio I. Mazilu and Nicholas Kolokotronis and Konstantinos Limniotis and Costas Vassilakis} } @article {828, title = {Chatbots for Cultural Venues: A Topic-Based Approach}, journal = {Algorithms}, volume = {16}, year = {2023}, month = {jul}, pages = {339}, abstract = {

Digital assistants{\textemdash}such as chatbots{\textemdash}facilitate the interaction between persons and machines and are increasingly used in web pages of enterprises and organizations. This paper presents a methodology for the creation of chatbots that offer access to museum information. The paper introduces an information model that is offered through the chatbot, which subsequently maps the museum{\textquoteright}s modeled information to structures of DialogFlow, Google{\textquoteright}s chatbot engine. Means for automating the chatbot generation process are also presented. The evaluation of the methodology is illustrated through the application of a real case, wherein we developed a chatbot for the Archaeological Museum of Tripolis, Greece.

}, keywords = {chatbots, cultural venues, Museums, RDF triples, topics}, doi = {10.3390/a16070339}, url = {https://doi.org/10.3390/a16070339}, author = {Vasilis Bouras and Dimitris Spiliotopoulos and Dionisis Margaris and Costas Vassilakis and Konstantinos Kotis and Angeliki Antoniou and George Lepouras and Manolis Wallace and Vassilis Poulopoulos} } @article {818, title = {Disruptive Technologies for Parliaments: A Literature Review}, journal = {Future Internet}, volume = {15}, year = {2023}, month = {feb}, pages = {66}, abstract = {

Exploitation and use of disruptive technologies, such as the Internet of Things, recommender systems, and artificial intelligence, with an ambidextrous balance, are a challenge, nowadays. Users of the technologies, and stakeholders, could be part of a new organisational model that affects business procedures and processes. Additionally, the use of inclusive participatory organisational models is essential for the effective adoption of these technologies. Such models aim to transform organisational structures, as well. Public organisations, such as the parliament, could utilise information systems{\textquoteright} personalisation techniques. As there are a lot of efforts to define the framework, the methodology, the techniques, the platforms, and the suitable models for digital technologies adoption in public organisations, this paper aims to provide a literature review for disruptive technology inclusive use in parliaments. The review emphasises the assessment of the applicability of the technologies, their maturity and usefulness, user acceptance, their performance, and their correlation to the adoption of relevant innovative, inclusive organisational models. It is argued that the efficient digital transformation of democratic institutions, such as parliaments, with the use of advanced e-governance tools and disruptive technologies, requires strategic approaches for adoption, acceptance, and inclusive service adaptation.

}, keywords = {artificial intelligence, digital transformation, inclusiveness, Internet of Things, parliament, Recommender Systems}, doi = {https://doi.org/10.3390/fi15020066}, url = {https://www.mdpi.com/1999-5903/15/2/66}, author = {Dimitris Koryzis and Dionisis Margaris and Costas Vassilakis and Konstantinos Kotis and Dimitris Spiliotopoulos} } @conference {822, title = {Minimizing Software-Rooted Risk through Library Implementation Selection}, booktitle = {Proceedings of the 2023 IEEE CSR Workshop on Data Science for Cyber Security (DS4CS)}, year = {2023}, abstract = {

In contemporary Internet of Things (IoT) systems, complex software artifacts are deployed to realize the required functionalities. The business logic of these software artifacts is uniquely composed through code that is customly developed according to the requirements, while all software artifacts utilize libraries that implement generic functionalities, which are needed in the context of the realized operations. Libraries, however, often entail vulnerabilities, which may be exploited by threat agents to attack the system. In many cases, the functionality required by an application is realized by a number of alternative libraries, with each library having its own list of vulnerabilities, while differentiations in other non-functional properties (e.g. execution efficiency, memory footprint etc.) may also be present. In this paper, we present an approach for automating the task of minimizing the risk level of IoT systems that is owing to the vulnerabilities of libraries required by software artifacts. The proposed approach exploits knowledge on which libraries provide equivalent implementations of the same functionalities, and automatically assesses the risk level of candidate library combinations and finally selects the library configuration exhibiting the minimum risk level to bundle into the executable software artifact. Additionally, the risk level of candidate implementations is constantly monitored for new vulnerability identifications or fixes in the implementations, triggering new risk assessments and producing new executables as appropriate. The proposed approach can be used in IoT platform deployment to minimize the software-rooted risk level.

}, author = {Panagiotis Sotiropoulos and Christos-Minas Mathas and Costas Vassilakis and Nicholas Kolokotronis} } @article {820, title = {Rating Prediction Quality Enhancement in Low-Density Collaborative Filtering Datasets}, journal = {Big Data and Cognitive Computing}, volume = {7}, year = {2023}, month = {mar}, pages = {59}, abstract = {

Collaborative filtering has proved to be one of the most popular and successful rating prediction techniques over the last few years. In collaborative filtering, each rating prediction, concerning a product or a service, is based on the rating values that users that are considered {\textquotedblleft}close{\textquotedblright} to the user for whom the prediction is being generated have given to the same product or service. In general, {\textquotedblleft}close{\textquotedblright} users for some user u correspond to users that have rated items similarly to u and these users are termed as {\textquotedblleft}near neighbors{\textquotedblright}. As a result, the more reliable these near neighbors are, the more successful predictions the collaborative filtering system will compute and ultimately, the more successful recommendations the recommender system will generate. However, when the dataset{\textquoteright}s density is relatively low, it is hard to find reliable near neighbors and hence many predictions fail, resulting in low recommender system reliability. In this work, we present a method that enhances rating prediction quality in low-density collaborative filtering datasets, by considering predictions whose features are associated with high prediction accuracy as additional ratings. The presented method{\textquoteright}s efficacy and applicability are substantiated through an extensive multi-parameter evaluation process, using widely acceptable low-density collaborative filtering datasets.

}, keywords = {collaborative filtering, Evaluation, low density datasets, personalization, rating prediction quality, Recommender Systems, reliability}, doi = {10.3390/bdcc7020059}, url = {https://www.mdpi.com/2504-2289/7/2/59}, author = {Dionisis Margaris and Costas Vassilakis and Dimitris Spiliotopoulos and Stefanos Ougiaroglou} } @article {823, title = {A Software Vulnerability Management Framework for the Minimization of System Attack Surface and Risk}, journal = {Electronics}, volume = {12}, year = {2023}, month = {may}, pages = {2278}, abstract = {

Current Internet of Things (IoT) systems comprise multiple software systems that are deployed to provide users with the required functionalities. System architects create system blueprints and draw specifications for the software artefacts that are needed; subsequently, either custom-made software is developed according to these specifications and/or ready-made COTS/open source software may be identified and customized to realize the overall system goals. All deployed software however may entail vulnerabilities, either due to insecure coding practices or owing to misconfigurations and unexpected interactions. Moreover, software artefacts may implement a much broader set of functionalities than may be strictly necessary for the system at hand, in order to serve a wider range of needs, and failure to appropriately configure the deployed software to include only the required modules results in the further increase of the system attack surface and the associated risk. In this paper, we present a software vulnerability management framework which facilitates (a) the configuration of software to include only the necessary features, (b) the execution of security-related tests and the compilation of platform-wide software vulnerability lists, and (c) the prioritization of vulnerability addressing, considering the impact of each vulnerability, the associated technical debt for its remediation, and the available security budget. The proposed framework can be used as an aid in IoT platform implementation by software architects, developers, and security experts.

}, keywords = {IoT systems, risk management, software vulnerabilities, system design, system security, technical debt}, doi = {10.3390/electronics12102278}, url = {https://doi.org/10.3390/electronics12102278}, author = {Panagiotis Sotiropoulos and Christos-Minas Mathas and Costas Vassilakis and Nicholas Kolokotronis} } @conference {830, title = {A User Interface for Tuning QoS parameters in Recommendation-Based Business Process Scenario Adaptation}, booktitle = {Proceedings of the 19th International Conference on Web Information Systems and Technologies (WEBIST 2023)}, year = {2023}, abstract = {

The Web Services Business Process Execution Language (BPEL) is a special-purpose language that orchestrates web services into a high-level business process. A typical BPEL scenario contains invocations to preselected web services, along with their parameters. However, many recent research works support dynamic service selection, based on user-set policies and criteria. Furthermore, users may request a service recommendation, in which case functionally equivalent service offerings by different providers will be considered by the personalization module. Along with the recommendation request, users provide the policy parameters, which include minimum and maximum bounds for the non-functional attributes concerning the service, and the system exploits these bounds to select and use the optimal candidate services. However, in many real-life cases, a person will accept/purchase a product or a service that exceeds the threshold(s) that initially he/she has set, e.g. if the overhead is marginal or the offer is deemed appealing, or no satisfactory service candidates are identified using the initial settings. In this paper, we present and evaluate a specialized User Interface that allows the user to review service candidates marginally exceeding the specified bounds, and consider them while making the final service selection.

}, keywords = {adaptation, business processes, Personalisation, Recommendation, Recommender Systems, UI, Web services, WS-BPEL}, author = {Kiriakos Sgardelis and Dionisis Margaris and Dimitris Spiliotopoulos and Costas Vassilakis} } @conference {810, title = {Anchoring Effect Mitigation for Complex Recommender System Design}, booktitle = {Proceedings of the 2022 HCI International Conference}, year = {2022}, keywords = {Information/knowledge design/visualization, mental-model, Metaphor, navigation design, Service Design}, author = {Dionisis Margaris and Dimitris Spiliotopoulos and Costas Vassilakis} } @conference {812, title = {Persona Finetuning for Online Gaming using Personalisation Techniques}, booktitle = {Proceedings of the 2022 HCII Conference}, year = {2022}, abstract = {

Automatic persona generation has been shown to have specific measurable benefits for application creators and users. In most situations, personas are adequately descriptive and diversified to achieve user type accuracy and coverage. For specific market segments, such as online gaming, using personas may accurately describe existing user base but not changing habit and need that are introduced by the fluidity of the offerings and the delivery methods. Changes in the ways that applications are marketed, such as new payment methods, for example, subscription models, pay-to-play and pay-to-win, payment-driven-gamification, seriously affect user needs and result in direct impact on user acceptance. This work utilises structured user needs from online gaming players to augment personas using personalisation techniques. The personas are finetuned and dediversified to result in concise personas, based on user needs that successfully convey information for creators and users alike.

}, keywords = {collaborative filtering, Data-driven methods, Diversification, Online Gaming, Persona, Personalisation, Usability evaluation, User study}, author = {Dimitris Spiliotopoulos and Dionisis Margaris and Kostas N. Koutrakis and Panos Philippopoulos and Costas Vassilakis} } @article {813, title = {On Producing Accurate Rating Predictions in Sparse Collaborative Filtering Datasets}, journal = {Information}, volume = {13}, year = {2022}, month = {jun}, pages = {302}, abstract = {

The typical goal of a collaborative filtering algorithm is the minimisation of the deviation between rating predictions and factual user ratings so that the recommender system offers suggestions for appropriate items, achieving a higher prediction value. The datasets on which collaborative filtering algorithms are applied vary in terms of sparsity, i.e., regarding the percentage of empty cells in the user{\textendash}item rating matrices. Sparsity is an important factor affecting rating prediction accuracy, since research has proven that collaborative filtering over sparse datasets exhibits a lower accuracy. The present work aims to explore, in a broader context, the factors related to rating prediction accuracy in sparse collaborative filtering datasets, indicating that recommending the items that simply achieve higher prediction values than others, without considering other factors, in some cases, can reduce recommendation accuracy and negatively affect the recommender system{\textquoteright}s success. An extensive evaluation is conducted using sparse collaborative filtering datasets. It is found that the number of near neighbours used for the prediction formulation, the rating average of the user for whom the prediction is generated and the rating average of the item concerning the prediction can indicate, in many cases, whether the rating prediction produced is reliable or not.

}, keywords = {accuracy, collaborative filtering, Evaluation, Personalisation, Rating prediction, Recommender Systems, reliability, Sparse Datasets}, doi = {https://doi.org/10.3390/info13060302}, author = {Dionisis Margaris and Costas Vassilakis and Dimitris Spiliotopoulos} } @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 = {Dionisis Margaris and Spiliotopoulos, Dimitris and Karagiorgos, Gregory and Costas Vassilakis and Vasilopoulos, Dionysios} } @article {803, title = {Augmenting Black Sheep Neighbour Importance for Enhancing Rating Prediction Accuracy in Collaborative Filtering}, journal = {Applied Sciences}, volume = {11}, year = {2021}, month = {sep}, pages = {8369}, abstract = {

In this work, an algorithm for enhancing the rating prediction accuracy in collaborative filtering, which does not need any supplementary information, utilising only the users{\textquoteright} ratings on items, is presented. This accuracy enhancement is achieved by augmenting the importance of the opinions of {\textquoteleft}black sheep near neighbours{\textquoteright}, which are pairs of near neighbours with opinion agreement on items that deviates from the dominant community opinion on the same item. The presented work substantiates that the weights of near neighbours can be adjusted, based on the degree to which the target user and the near neighbour deviate from the dominant ratings for each item. This concept can be utilized in various other CF algorithms. The experimental evaluation was conducted on six datasets broadly used in CF research, using two user similarity metrics and two rating prediction error metrics. The results show that the proposed technique increases rating prediction accuracy both when used independently and when combined with other CF algorithms. The proposed algorithm is designed to work without the requirements to utilise any supplementary sources of information, such as user relations in social networks and detailed item descriptions. The aforesaid point out both the efficacy and the applicability of the proposed work.

}, keywords = {black sheep users, collaborative filtering, Evaluation, rating prediction accuracy}, doi = {10.3390/app11188369}, url = {https://doi.org/10.3390/app11188369}, author = {Dionisis Margaris and Dimitris Spiliotopoulos and Costas Vassilakis} } @article {798, title = {On the Design of IoT Security: Analysis of Software Vulnerabilities for Smart Grids}, journal = {Energies}, volume = {14}, year = {2021}, month = {may}, pages = {2818}, abstract = {

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.

}, keywords = {Internet of Things, robustness, security, software vulnerabilities}, doi = {10.3390/en14102818}, author = {Christos-Minas Mathas and Costas Vassilakis and Nicholas Kolokotronis and Charilaos C. Zarakovitis and Michail-Alexandros Kourtis} } @mastersthesis {855, title = {Emotion analysis for videos with Greek speech}, year = {2021}, month = {10/2021}, pages = {44}, type = {Undergraduate}, abstract = {

Opinions play a major role to almost all human activities, because they are key influencers of our choices and way of thinking. Whenever we need to make a decision, we want to know others{\textquoteright} opinions on the subject at hand. In the real world, businesses
and organizations want to know consumer or public opinions about their products and services. On the other hand, individual consumers also want to know the opinions of
other people that have used a particular product, before purchasing it.


With the explosive growth of social networks (e.g. blogs, micro-blogs, forum, reviews, videos, comments and postings in social network sites) on the Web, a huge dataset has
been created, ready to be exploited to find user preferences and opinions. Sentiment analysis focuses on the task of automatically identifying whether a piece of text
expresses a positive or negative polarity about the subject matter. In this thesis, sentiment analysis is performed on Greek review and unboxing videos that are related
to tech products (mostly smartphones and laptops), using a sentiment lexicon. In addition, previous work in this area on Greek and the English languages is examined and compared.

}, author = {Sing Manvir and Costas Vassilakis} } @inbook {808, title = {Identifying Reliable Recommenders in Users{\textquoteright} Collaborating Filtering and Social Neighbourhoods}, booktitle = {Lecture Notes in Social Networks}, year = {2021}, pages = {51{\textendash}76}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, abstract = {

Recommender systems increasingly use information sourced from social networks to improve the quality of their recommendations. However, both recommender systems and social networks exhibit phenomena under which information for certain users or items is limited, such as the cold start and the grey sheep phenomena in collaborative filtering systems and the isolated users in social networks. In the context of a social network-aware collaborative filtering, where the collaborating filtering- and social network{\textendash}based neighbourhoods are of varying density and utility for recommendation formulation, the ability to identify the most reliable recommenders from each neighbourhood for each user and appropriately combine the information associated with them in the recommendation computation process can significantly improve the quality and accuracy of the recommendations offered. In this chapter, we report on our extensions on earlier works in this area which comprise of (1) the development of an algorithm for discovering the most reliable recommenders of a social network recommender system and (2) the development and evaluation of a new collaborative filtering algorithm that synthesizes the opinions of a user{\textquoteright}s identified recommenders to generate successful recommendations for the particular user. The proposed algorithm introduces significant gains in rating prediction accuracy (4.9\% on average, in terms of prediction MAE reduction and 4.2\% on average, in terms of prediction RMSE reduction) and outperforms other algorithms. The proposed algorithm, by design, utilizes only basic information from the collaborative filtering domain (user{\textendash}item ratings) and the social network domain (user relationships); therefore, it can be easily applied to any social network recommender system dataset.

}, keywords = {collaborative filtering, Evaluation, Limited Information, Near Neighbours, Rating prediction, Recommender Systems, Social Networks}, doi = {10.1007/978-3-030-67044-3_3}, author = {Dionisis Margaris and Dimitris Spiliotopoulos and Costas Vassilakis} } @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 Costas Vassilakis and Wallace, Manolis and Lantzouni, Marina and Poulopoulos, Vassilis and Dionisis Margaris} } @inbook {807, title = {Safe Travelling Period Recommendation to High Attack Risk European Destinations Based on Past Attack Information}, booktitle = {Lecture Notes in Social Networks}, year = {2021}, pages = {77{\textendash}100}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, abstract = {

Terrorism is a significant deterrent for tourism. It affects both visitors and local citizens and personnel of a country or area. On one hand, the potential visitor will probably avoid travelling to a high attack risk country, due to safety reasons, hence will miss the opportunity to visit it, and, on the other hand, the country{\textquoteright}s tourism will decline. This work addresses the aforementioned problem by (1) showing that relatively safe visiting periods for high attack risk European countries can be predicted with high accuracy, using limited information, comprising of attack and fatality data from the past years, which are widely available, and (2) developing an algorithm that recommends relatively safe periods to potential travellers. The results of this work will be useful for tourists, visitors, businesses and operators, as well as relevant stakeholders and actors.

}, keywords = {Evaluation, Recommendation algorithm, Risk Calculation, Safety Perception, Safety prediction, Terrorist Attacks, tourism}, doi = {10.1007/978-3-030-67044-3_3}, author = {Dimitris Spiliotopoulos and Dionisis Margaris and Costas Vassilakis} } @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 = {Dionisis Margaris and Spiliotopoulos, Dimitris and Vasilopoulos, Dionysios and Costas Vassilakis} } @article {771, title = {An Adaptive Social Network-Aware Collaborative Filtering Algorithm for Improved Rating Prediction Accuracy}, journal = {IEEE Access}, volume = {8}, year = {2020}, pages = {68301{\textendash}68310}, abstract = {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{\textquoteright} ratings on items and users{\textquoteright} 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{\textquoteright}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}, keywords = {Performance evaluation, Recommender Systems, Social computing}, issn = {2169-3536}, doi = {10.1109/access.2020.2981567}, author = {Dionisis Margaris and Anna Kobusinska and Dimitris Spiliotopoulos and Costas Vassilakis} } @article {779, title = {An Algorithm for Density Enrichment of Sparse Collaborative Filtering Datasets Using Robust Predictions as Derived Ratings}, journal = {Algorithms}, volume = {13}, year = {2020}, month = {07/2020}, pages = {174}, abstract = {

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.

}, keywords = {collaborative filtering, density enrichment, derived ratings, rating prediction accuracy, rating prediction coverage, Recommender Systems, robust predictions, Sparse Datasets}, doi = {https://doi.org/10.3390/a13070174}, author = {Dionisis Margaris and Dimitris Spiliotopoulos and Gregory Karagiorgos and Costas Vassilakis} } @article {782, title = {Data-Assisted Persona Construction Using Social Media Data}, journal = {Big Data and Cognitive Computing}, volume = {4}, year = {2020}, month = {aug}, pages = {21}, abstract = {

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.

}, keywords = {Cultural Events, Persona, personalization, Topic Modelling, Usability, User Experience, User Interface Design}, doi = {10.3390/bdcc4030021}, author = {Dimitris Spiliotopoulos and Dionisis Margaris and Costas Vassilakis} } @article {723, title = {A Fine-grained Social Network Recommender System}, journal = {Social Network Analysis and Mining}, volume = {10}, year = {2020}, abstract = {Recommender systems have greatly evolved in recent years and have become an integral part of the Web. From e-commerce sites to mobile apps, our daily routine revolves around a series of {\textquotedblleft}small{\textquotedblright} decisions that are influenced by such recommendations. In a similar manner, online social networks recommend only a subset of the massive amount of content published by a user{\textquoteright}s friends. However, the prevalent approach for the content selection process in such systems is driven by the amount of interaction between the user and the friend who published the content. As a result, content of interest is often lost due to weak social ties. In this paper, we present a fine-grained recommender system for social ecosystems, designed to recommend media content (e.g., music videos, online clips) published by the user{\textquoteright}s friends. The system design was driven by the findings of our qualitative user study that explored the value and requirements of a recommendation component within a social network. The core idea behind the proposed approach was to leverage the abundance of preexisting information in each user{\textquoteright}s account for creating interest profiles, to calculate similarity scores at a fine-grained level for each friend. The intuition behind the proposed method was to find consistent ways to obtain information representations that can identify overlapping interests in very specific sub-categories (e.g., two users{\textquoteright} music preferences may only coincide on hard rock). While the system is intended as a component of the social networking service, we developed a proof-of-concept implementation for Facebook and explored the effectiveness of our underlying mechanisms for content analysis. Our experimental evaluation demonstrates the effectiveness of our approach, as the recommended content of interest was both overlooked by the existing Facebook engine and not contained in the users{\textquoteright} Facebook News Feed. We also conducted a user study for exploring the usability aspects of the prototype and found that it offers functionality that could significantly improve user experience in popular services.}, keywords = {Content Analysis, Interest Profiles, Recommender Systems, Social Networks, Usability}, issn = {1869-5469}, doi = {10.1007/s13278-019-0621-7}, author = {Markos Aivazoglou and Antonios Roussos and Dionisis Margaris and Costas Vassilakis and Sotiris Ioannidis and Jason Polakis and Dimitris Spiliotopoulos} } @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 Costas Vassilakis and Dionisis Margaris} } @article {margaris-IJBDI-2019-b, title = {Improving collaborative filtering{\textquoteright}s rating prediction accuracy by considering users{\textquoteright} dynamic rating variability}, journal = {International Journal of Big Data Intelligence}, volume = {7}, year = {2020}, chapter = {59--71}, abstract = {

Users that populate ratings databases, follow different marking practices, in the sense that some are stricter, while others are more lenient. Similarly, users{\textquoteright} rating practices may also differ in rating variability, in the sense that some users may be entering ratings close to their mean, while other users may be entering more extreme ratings, close to the limits of the rating scale. While this aspect has been recently addressed through the computation and exploitation of an overall rating variability measure per user, the fact that user rating practices may vary along the user{\textquoteright}s rating history time axis may render the use of the overall rating variability measure inappropriate for performing the rating prediction adjustment. In this work, we (1) propose an algorithm that considers two variability metrics per user, the global (overall) and the local one, with the latter representing the user{\textquoteright}s variability at prediction time, (2) present alternative methods for computing a user{\textquoteright}s local variability and (3) evaluate the performance of the proposed algorithm in terms of rating prediction quality and compare it against the state-of-the-art algorithm that employs a single variability metric in the rating prediction computation process.

}, keywords = {collaborative filtering, Cosine Similarity, Evaluation, Pearson Correlation Coefficient, prediction accuracy, users{\textquoteright} ratings dynamic variability}, doi = {10.1504/ijbdi.2020.107373}, author = {Dionisis Margaris and Costas Vassilakis} } @article {margaris-IJBDI-2019-a, title = {Improving collaborative filtering{\textquoteright}s rating prediction coverage in sparse datasets by exploiting the {\textquotedblleft}friend of a friend{\textquotedblright} concept}, journal = {International Journal of Big Data Intelligence}, volume = {7}, year = {2020}, chapter = {47}, abstract = {

Collaborative filtering computes personalized recommendations by taking into account ratings expressed by users. Collaborative filtering algorithms firstly identify people having similar tastes, by examining the likeness of already entered ratings. Users with highly similar tastes are termed {\textquotedblleft}near neighbours{\textquotedblright} and recommendations for a user are based on her near neighbours{\textquoteright} ratings. However, for a number of users no near neighbours can be found, a problem termed as the {\textquotedblleft}gray sheep{\textquotedblright} problem. This problem is more intense in sparse datasets, i.e. datasets with relatively small number of ratings, compared to the number of users and items. In this work, we propose an algorithm for alleviating this problem by exploiting the friend of a friend (FOAF) concept. The proposed algorithm, CFfoaf, has been evaluated against eight widely used sparse datasets and under two widely used collaborative filtering correlation metrics, namely the Pearson Correlation Coefficient and the Cosine Similarity and has been proven to be particularly effective in increasing the percentage of users for which personalized recommendations can be formulated in the context of sparse datasets, while at the same time improving rating prediction quality.

}, keywords = {collaborative filtering, Cosine Similarity, Evaluation, friend-of-a-friend, Pearson Correlation Coefficient, prediction accuracy, Recommender Systems, Sparse Datasets}, issn = {2053-1389}, doi = {10.1504/ijbdi.2020.106178}, author = {Dionisis Margaris and Costas Vassilakis} } @conference {784, title = {MOOC Accessibility from the Educator Perspective}, booktitle = {Proceedings of the 22nd International Conference on Human-Computer Interaction}, year = {2020}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, abstract = {

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.

}, keywords = {design-for-all, interactive e-learning, MOOC, speech processing, technologies for accessibility, universal accessibility}, url = {10.1007/978-3-030-60149-2_10}, author = {Dimitris Spiliotopoulos and Vassilis Poulopoulos and Dionisis Margaris and Eleni Makri and Costas Vassilakis} } @article {781, title = {Multimodal Interaction: Correlates of Learners{\textquoteright} Metacognitive Skill Training Negotiation Experience}, journal = {Information}, volume = {11}, year = {2020}, month = {jul}, pages = {381}, abstract = {

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.

}, keywords = {big data, Cultural Events, Persona, personalization, Topic Modelling, Usability, User Experience, User Interface Design}, doi = {10.3390/info11080381}, author = {Dimitris Spiliotopoulos and Eleni Makri and Costas Vassilakis and Dionisis Margaris} } @conference {786, title = {Policy Making Analysis and Practitioner User Experience}, booktitle = {Proceedings of the 22nd International Conference on Human-Computer Interaction}, year = {2020}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, abstract = {

This article presents the work on social media analysis-driven policy-making platforms that are powered by classic social media analysis technologies, such as policy modelling, linguistic analysis, opinion mining, sentiment analysis and information visualization. The approach examines the user design perspective towards user experience in policymaking for all the innovative modules used. The technology behind such complex task is presented while the resulting platform is appraised on the potential for real world application. The findings drive the development and the requirements for the summative usability assessment tests. We also report on the level the practitioners adopted the policy formulation tools.

}, keywords = {Content Analysis, natural language interfaces, opinion mining, policy making, social network analysis, User Experience}, doi = {10.1007/978-3-030-60114-0_29}, author = {Dimitris Koryzis and Fotios Fitsilis and Dimitris Spiliotopoulos and Theocharis Theocharopoulos and Dionisis Margaris and Costas Vassilakis} } @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 Costas Vassilakis}, editor = {Nicholas Kolokotronis and Stavros Shiaeles} } @article {770, title = {A Semantic Mixed Reality Framework for Shared Cultural Experiences Ecosystems}, journal = {Big Data and Cognitive Computing}, volume = {4}, year = {2020}, month = {4/2020}, pages = {6}, abstract = {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.}, keywords = {cultural experience, intelligent interaction, Mixed Reality, semantics, Usability}, doi = {https://doi.org/10.3390/bdcc4020006}, author = {Costas Vassilakis and Konstantinos Kotis and Dimitris Spiliotopoulos and Dionisis Margaris and Vlasios Kasapakis and Christos-Nikolaos Anagnostopoulos and Georgios Santipantakis and George A. Vouros and Theodore Kotsilieris and Volha Petukhova and Andrei Malchanau and Ioanna Lykourentzou and Kaj Michael Helin and Artem Revenko and Nenad Gligoric and Boris Pokric} } @conference {777, title = {Semantics-Driven Conversational Interfaces for Museum Chatbots}, booktitle = {Proceedings of the 22nd International Conference on Human-Computer Interaction, volume "Culture and Computing"}, year = {2020}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, abstract = {

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.

}, keywords = {chatbots, conversational interfaces, cultural technology, semantics}, doi = {10.1007/978-3-030-50267-6_20}, author = {Dimitris Spiliotopoulos and Konstantinos Kotis and Costas Vassilakis and Dionisis Margaris} } @conference {778, title = {{\textquotedblleft}Talking{\textquotedblright} Triples to Museum Chatbots}, booktitle = {Proceedings of the 22nd International Conference on Human-Computer Interaction, volume "Culture and Computing"}, year = {2020}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, abstract = {

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{\textquoteright} 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.

}, keywords = {chatbots, knowledge graphs, Museums, NLP, RDF triples}, doi = {10.1007/978-3-030-50267-6_22}, author = {Savvas Varitimiadis and Konstantinos Kotis and Dimitris Spiliotopoulos and Costas Vassilakis and Dionisis Margaris} } @conference {783, title = {Threat landscape for smart grid systems}, booktitle = {Proceedings of the 15th International Conference on Availability, Reliability and Security}, year = {2020}, month = {jul}, publisher = {ACM}, organization = {ACM}, abstract = {

Smart Grids are energy delivery networks, constituting an evolution of power grids, in which a bidirectional flow between power providers and consumers is established. These flows support the transfer of electricity and information, in order to support automation actions in the context of the energy delivery network. Insofar, many smart grid implementations and implementation proposals have emerged, with varying degrees of feature delivery and sophistication. While smart grids offer many advantages, their distributed nature and information flow streams between energy producers and consumers enable the launching of a number of attacks against the smart grid infrastructure, where the related consequences may range from economic loss to complete failure of the smart grid. In this paper, we survey the threat landscape of smart grids, identifying threats that are specific to this infrastructure, providing an assessment of the severity of the consequences of each attack type, discerning features that can be utilized to detect attacks and listing methods that can be used to mitigate them.

}, keywords = {attack detection features, attack mitigation, attacks, security, Smart grids}, doi = {https://doi.org/10.1145/3407023.3409229}, author = {Christos-Minas Mathas and Konstantinos-Panagiotis Grammatikakis and Costas Vassilakis and Nicholas Kolokotronis and Vasiliki-Georgia Bilali and Dimitris Kavallieros} } @conference {789, title = {A Trust Management System for the IoT domain}, booktitle = {Proceedings the 2020 IEEE World Congress on Services (SERVICES)}, year = {2020}, publisher = {IEEE}, organization = {IEEE}, abstract = {

In modern internet-scale computing, interaction between a large number of parties that are not known a-priori is predominant, with each party functioning both as a provider and consumer of services and information. In such an environment, traditional access control mechanisms face considerable limitations, since granting appropriate authorizations to each distinct party is infeasible both due to the high number of grantees and the dynamic nature of interactions. Trust management has emerged as a solution to this issue, offering aids towards the automated verification of actions against security policies. In this paper, we present a trust- and risk-based approach to security, which considers status, behavior and associated risk aspects in the trust computation process, while additionally it captures user-to-user trust relationships which are propagated to the device level, through user-to-device ownership links.

}, keywords = {associated risk, behavior, Internet-of-things, status, trust management}, doi = {10.1109/SERVICES48979.2020.00047}, author = {Christos-Minas Mathas and Costas Vassilakis and Nicholas Kolokotronis} } @conference {785, title = {A User Interface for Personalized Web Service Selection in Business Processes}, booktitle = {Lecture Notes in Computer Science}, year = {2020}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, abstract = {

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.

}, keywords = {adaptation, business processes, personalization, user interface, Web services, WS-BPEL}, doi = {10.1007/978-3-030-60152-2_41}, author = {Dionisis Margaris and Dimitris Spiliotopoulos and Costas Vassilakis and Gregory Karagiorgos} } @article {775, title = {What makes a review a reliable rating in recommender systems?}, journal = {Information Processing \& Management}, volume = {57}, year = {2020}, month = {04/2020}, pages = {102304}, abstract = {

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{\textquoteright}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{\textquoteright} 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.

}, keywords = {collaborative filtering, Confidence level, Feature selection, Rating prediction using textual information, Recommender Systems}, doi = {https://doi.org/10.1016/j.ipm.2020.102304}, author = {Dionisis Margaris and Costas Vassilakis and Dimitris Spiliotopoulos} } @article {715, title = {Bringing a peripheral, traditional venue to the digital era with targeted narratives}, journal = {Digital Applications in Archaeology and Cultural Heritage}, volume = {14}, year = {2019}, month = {sep}, pages = {e00111}, abstract = {The current work presents a method of enhancing museum content with narratives targeting history reflection in a personalized manner, delivered by an especially designed mobile app. The mobile app is a part of a technology ecosystem that supports the visitor before, during and after her visit. Within the framework of the European H2020 CrossCult project, a method was designed, implemented and tested to enhance museum content with digital material, enriched narratives and means to actively engage visitors. In particular, the method was implemented and tested for the content of the Archaeological Museum of Tripolis (Greece) and specific content examples are presented here, together with data from visitor evaluation. The results showed that content was significantly and qualitatively enhanced and visitors recognized the potential of the app in triggering visitor curiosity, new forms of engagement, and the ability to reflect on the connections between historical and social phenomena.}, keywords = {Cultural heritage, Museum app, Narratives, reflection, Visitor experience}, doi = {10.1016/j.daach.2019.e00111}, url = {https://www.sciencedirect.com/science/article/pii/S2212054819300098}, author = {Angeliki Antoniou and Susana Reboreda Morillo and George Lepouras and Jason Diakoumakos and Costas Vassilakis and Martin Lopez Nores and Catherine Emma Jones} } @conference {717, title = {Citizen Engagement for Transparent and Accountable Policy Modelling}, booktitle = {Proceedings of the 11th International Conference on Management of Digital EcoSystems (MEDES{\textquoteright}19)}, year = {2019}, publisher = {ACM}, organization = {ACM}, abstract = {In the modern information society, learning is no longer just about obtaining factual knowledge, but more about general skills on how and where to apply available knowledge and obtain new knowledge in order to solve new problems. Such skills include abilities to connect and organize ideas, fill gaps in knowledge structures, evaluate evidence, argue with new information, test and modify, predict, clarify, generate questions, learn new concepts, make unexpected connections, reflect, analyze, synthesize and loop back. This work presents the Immersion framework, a digital ecosystem for adaptive smart learning environments for interactive mixed reality driven methods to foster learners{\textquoteright} self-regulating skill development.}, keywords = {Game-based Interaction, Learner Collaboration, Mixed Reality, Mobile Learning, Self-regulated Learning, Technology Enhanced Learning}, author = {Dimitris Spiliotopoulos and Dionisis Margaris and Costas Vassilakis} } @conference {spiliotopoulos_2019_asonam, title = {Data-driven Country Safety Monitoring Terrorist Attack Prediction}, booktitle = {Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2019)}, year = {2019}, month = {aug}, abstract = {Terrorism is a key risk for prospective visitors of tourist desti-nations. This work reports on the analysis of past terrorist at-tack data, focusing on tourist-related attacks and attack types in Mediterranean EU area and the development of algorithms to predict terrorist attack risk levels. Data on attacks in 10 coun-tries have been analyzed to quantify the threat level of tourism-related terrorism based on the data from 2000 to 2017 and for-mulate predictions for subsequent periods. Results show that predictions on potential target types can be derived with ade-quate accuracy. Such results are useful for initiating, shifting and validating active terrorism surveillance based on real pre-dicted attack and target types per country.}, keywords = {Data Analysis, Risk Calculation, Safety Perception, Terrorist Attacks, Threat Ranking}, author = {Dimitris Spiliotopoulos and Costas Vassilakis and Dionisis Margaris} } @article {SODA-TR-19003, title = {Experimental results for considering Item Adoption Eagerness Information in Collaborative Filtering{\textquoteright}s Rating Prediction}, year = {2019}, month = {07/2019}, publisher = {Software and Database Systems Lab, University of the Peloponnese}, type = {Technical report}, address = {Tripolis}, abstract = {In this technical report, we present the experimental findings from applying an algorithm that considers Item Adoption Eagerness Information in the rating prediction formulation process, in order to increase rating prediction quality in Collaborative Filtering (CF). To this end, the algorithm is applied to seven datasets, which are widely used in recommender system (RS) research. The results indicate that the above algorithm introduces considerable prediction accuracy gains. }, url = {https://soda.dit.uop.gr/?q=TR-19003}, author = {Dionisis Margaris and Dimitris Spiliotopoulos and Costas Vassilakis} } @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 = {Dionisis Margaris and Costas Vassilakis and Spiliotopoulos, Dimitris} } @conference {margarisIISA_CIRPaC, title = {Improving Collaborative Filtering{\textquoteright}s Rating Prediction Accuracy by Introducing the Common Item Rating Past Criterion}, booktitle = {Proceedings of the 10th International Conference on Information, Intelligence, Systems and Applications (IISA2019)}, year = {2019}, month = {jul}, abstract = {Collaborative filtering computes personalized recommendations by taking into account ratings expressed by users. Collaborative filtering algorithms firstly identify people having similar tastes, by examining the likeness of already entered ratings. Users with highly similar tastes are termed {\textquotedblleft}near neighbors{\textquotedblright} and recommendations for a user are based on his near neighbors{\textquoteright} ratings. In order to measure similarity between users, so as to determine a user{\textquoteright}s NNs, a similarity metric is used. Insofar, similarity metrics proposed in the literature either consider all user ratings equally or take into account temporal variations within the users{\textquoteright} or items{\textquoteright} ratings history. However users{\textquoteright} ratings are co-shaped according to the experiences that they had in the past; therefore if two users enter similar (or dissimilar) ratings for an item while having experienced {\textendash}to a large extent the same items in the past, this constitutes stronger evidence about user similarity (or dissimilarity). Insofar however, no similarity metric takes into account this aspect. In this work, we (1) propose an algorithm that considers the common item rating past in the rating prediction computation process, aiming to improve rating prediction quality, and (2) evaluate the proposed algorithm against seven widely used datasets, both dense and sparse, and considering two widely used user similarity metrics.}, keywords = {collaborative filtering, Common Item Rating Past Criterion, Cosine Similarity, Evaluation, Items{\textquoteright} Rating Sequence, Pearson Correlation Coeffi-cient}, author = {Dionisis Margaris and Dionysios Vasilopoulos and Costas Vassilakis and Dimitris Spiliotopoulos} } @conference {margaris_iisa2019_VNNs, title = {Improving Collaborative Filtering{\textquoteright}s Rating Prediction Coverage in Sparse Datasets through the Introduction of Virtual Near Neighbors}, booktitle = {Proceedings of the 10th International Conference on Information, Intelligence, Systems and Applications (IISA2019)}, year = {2019}, month = {jul}, abstract = {Collaborative filtering creates personalized recommendations by considering ratings entered by users. Collaborative filtering algorithms initially detect users whose likings are alike, by ex-ploring the similarity between ratings that have insofar been submitted. Users having a high degree of similarity regarding their ratings are termed near neighbors, and in order to formu-late a recommendation for a user, her near neighbors{\textquoteright} ratings are extracted and form the basis for the recommendation. Col-laborative filtering algorithms however exhibit the problem commonly referred to as {\textquotedblleft}gray sheep{\textquotedblright}: this pertains to the case where for some users no near neighbors can be identified, and hence no personalized recommendations can be computed. The {\textquotedblleft}gray sheep{\textquotedblright} problem is more severe in sparse datasets, i.e. datasets where the number of ratings is small, compared to the number of items and users. In this paper, we address the {\textquotedblleft}gray sheep{\textquotedblright} problem by introducing the concept of virtual near neighbors and a related algorithm for their creation on the basis of the existing ones. We evaluate the proposed algorithm, which is termed as CFVNN, using eight widely used datasets and consid-ering two correlation metrics which are widely used in Collabo-rative Filtering research, namely the Pearson Correlation Coef-ficient and the Cosine Similarity. The results show that the pro-posed algorithm considerably leverages the capability of a Col-laborative Filtering system to compute personalized recommen-dations in the context of sparse datasets, tackling thus efficiently the {\textquotedblleft}gray sheep{\textquotedblright} problem. In parallel, the CFVNN algorithm achieves improvements in rating prediction quality, as this is expressed through the Mean Absolute Error and the Root Mean Square Error metrics.}, keywords = {collaborative filtering, Cosine Similarity, Evaluation, Pearson Correlation Coefficient, Sparse Datasets, Virtual Near Neighbors}, author = {Dionisis Margaris and Dionysios Vasilopoulos and Costas Vassilakis and Dimitris Spiliotopoulos} } @conference {716, title = {Improving Collaborative Filtering{\textquoteright}s Rating Prediction Quality by Exploiting the Item Adoption Eagerness Information}, booktitle = {Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence}, year = {2019}, publisher = {ACM}, organization = {ACM}, abstract = {Collaborative filtering computes personalized recommendations by taking into account ratings expressed by users. Collaborative filtering algorithms firstly identify people having similar tastes, who are termed as {\textquotedblleft}near neighbors" and recommendations for a user, are based on his near neighbors{\textquoteright} ratings. On the other hand, people exhibit different levels of eagerness to adopt new products: according to this characteristic, there is a set of users, termed as {\textquotedblleft}Early Adopters", who are prone to start using a product or technology as soon as it becomes available, in contrast to the majority of users, who prefer to start using items once they reach maturity; this important aspect of user behavior is not taken into account by existing algorithms. In this work, we (1) propose an algorithm that considers the eagerness shown by users to adopt products, aiming to improve rating prediction quality and (2) evaluate the proposed algorithm against seven widely used datasets.}, keywords = {collaborative filtering, Cosine Similarity, Evaluation, item adoption eagerness, Pearson Correlation Coefficient, rating prediction quality}, isbn = {978-1-4503-6934-3}, doi = {10.1145/3350546.3352544}, author = {Dionisis Margaris and Dimitris Spiliotopoulos and Costas Vassilakis} } @conference {spiliot_iisa2019, title = {A Methodology for Generated Text Annotation for High Quality Speech Synthesis}, booktitle = {Proceedings of the 10th International Conference on Information, Intelligence, Systems and Applications (IISA2019)}, year = {2019}, month = {jul}, abstract = {Natural Language Generators may produce linguistically en-riched text descriptions that can lead to significantly improved quality of speech synthesis. There are cases, however, where either the generator modules produce pieces of non-analysed, non-annotated plain text, or such modules are not available at all. Moreover, the language analysis is restricted by the usually limited domain coverage of the generator. For those cases the enriched input to the speech synthesizer needs to be produced from plain text in order to maintain speech quality. This work reports on the method for automatic domain dependent annota-tion of plain text, through the utilisation of the linguistic infor-mation from rich generated text. The synthetic speech from the resulting prosody models is evaluated by human participants showing annotation results for plain text quite on par with the rich generated text. This leads to improved perceived natural-ness of the synthesized speech.}, keywords = {Natural language generation, Natural language processing, Prosody enrichment, Semantic feature annotation, Text-to-speech}, author = {Dimitris Spiliotopoulos and Costas Vassilakis and Dionisis Margaris and Konstantinos Kotis} } @conference {718, title = {A Mixed-reality Interaction-driven Game-based Learning Framework}, booktitle = {Proceedings of the 11th International Conference on Management of Digital EcoSystems (MEDES{\textquoteright}19)}, year = {2019}, publisher = {ACM}, organization = {ACM}, abstract = {This work presents a platform for linked legislative data to engage citizens in transparent and effective democracies. With a focus on scaling up participatory approaches from local to national level, the approach extends well established and open source tools and technologies, to build mobile monitoring and analysis tools that increase transparency of law-making and implementation to citizens. This is achieved by combining open data and open services with user and citizen generated content, in order to address citizen{\textquoteright}s needs in the context of open government. Data and feeds from trusted sources are interconnected with new and re-purposed data feeds generated by users via the social web to form a meaningful, searchable, customizable, re-usable and open data-focused personalised mobile public service approach. The framework exploits the social aspects of open data, as well as the training of users, citizens and public servants to be able to understand and demand useful public open data, as well as facilitate the opening of more data.}, keywords = {Accountability, Citizen Engagement, e-government, Legislation, Mobile Public Services, Natural language processing, Policy Modelling, Transparency}, author = {Dimitris Spiliotopoulos and Dionisis Margaris and Costas Vassilakis and Volha Petukhova and Konstantinos Kotis} } @conference {Margaris_2019_asonam, title = {Social Relations versus Near Neighbours: Reliable Recommenders in Limited Information Social Network Collaborative Filtering for Online Advertising}, booktitle = {Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2019)}, year = {2019}, month = {aug}, abstract = {Online advertising benefits by recommender systems since the latter analyse reviews and rating of products, providing useful insight of the buyer perception of products and services. When traditional recommender system information is enriched with social network information, more successful recommendations are produced, since more users{\textquoteright} aspects are taken into consid-eration. However, social network information may be unavaila-ble since some users may not have social network accounts or may not consent to their use for recommendations, while rating data may be unavailable due to the cold start phenomenon. In this paper, we propose an algorithm that combines limited col-laborative filtering information, comprised only of users{\textquoteright} ratings on items, with limited social network information, com-prised only of users{\textquoteright} social relations, in order to improve (1) prediction accuracy and (2) prediction coverage in collaborative filtering recommender systems, at the same time. The proposed algorithm considerably improves rating prediction accuracy and coverage, while it can be easily integrated in recommender systems.}, keywords = {collaborative filtering, Evaluation, Limited Information, Near Neighbours, Online Advertising, Pearson Correlation Coefficient, Social Networks}, author = {Dionisis Margaris and Dimitris Spiliotopoulos and Costas Vassilakis} } @article {margaris2018tldks, title = {Enhancing Rating Prediction Quality Through Improving the Accuracy of Detection of Shifts in Rating Practices}, journal = {Lecture Notes in Computer Science}, volume = {10940 (LNCS)}, number = {XXXVII}, year = {2018}, pages = {151{\textendash}191}, publisher = {Springer Berlin Heidelberg}, abstract = {The most widely used similarity metrics in collaborative filtering, namely the Pearson Correlation and the Adjusted Cosine Similarity, adjust each individual rating by the mean of the ratings entered by the specific user, when computing similarities, due to the fact that users follow different rating practices, in the sense that some are stricter when rating items, while others are more lenient. However, a user{\textquoteright}s rating practices change over time, i.e. a user could start as lenient and subsequently become stricter or vice versa; hence by relying on a single mean value per user, we fail to follow such shifts in users{\textquoteright} rating practices, leading to decreased rating prediction accuracy. In this work, we present a novel algorithm for calculating dynamic user averages, i.e. time-in-point averages that follow shifts in users{\textquoteright} rating practices, and exploit them in both user-user and item-item collaborative filtering implementations. The proposed algorithm has been found to introduce significant gains in rating prediction accuracy, and outperforms other dynamic average computation approaches that are presented in the literature.}, doi = {10.1007/978-3-662-57932-9_5}, author = {Dionisis Margaris and Costas Vassilakis} } @conference {mathas2018a, title = {Evaluation of Apache Spot\textquotesingles machine learning capabilities in an SDN/NFV enabled environment}, booktitle = {Proceedings of the 13th International Conference on Availability, Reliability and Security - ARES 2018}, year = {2018}, publisher = {ACM Press}, organization = {ACM Press}, abstract = {Software Defined Networking (SDN) and Network Function Virtualisation (NFV) are transforming modern networks towards a service-oriented architecture. At the same time, the cybersecurity industry is rapidly adopting Machine Learning (ML) algorithms to improve detection and mitigation of complex attacks. Traditional intrusion detection systems perform signature-based detection, based on well-known malicious traffic patterns that signify potential attacks. The main drawback of this method is that attack patterns need to be known in advance and signatures must be preconfigured. Hence, typical systems fail to detect a zero-day attack or an attack with unknown signature. This work considers the use of machine learning for advanced anomaly detection, and specifically deploys the Apache Spot ML framework on an SDN/NFV-enabled testbed running cybersecurity services as Virtual Network Functions (VNFs). VNFs are used to capture traffic for ingestion by the ML algorithm and apply mitigation measures in case of a detected anomaly. Apache Spot utilises Latent Dirichlet Allocation to identify anomalous traffic patterns in Netflow, DNS and proxy data. We evaluate the overall performance of Apache Spot by deploying Denial of Service (Slowloris, BoNeSi) and a Data Exfiltration attack (iodine).}, keywords = {Apache Spot, Latent Dirichlet Allocation, Machine Learning, Network Function Virtualisation, Penetration Testing, SHIELD Project�, Software Defined Networking}, doi = {10.1145/3230833.3233278}, author = {Christos M. Mathas and Olga E. Segou and Georgios Xylouris and Dimitris Christinakis and Michail-Alexandros Kourtis and Costas Vassilakis and Anastasios Kourtis} } @article {margaris2018a, title = {Exploiting Rating Abstention Intervals for Addressing Concept Drift in Social Network Recommender Systems}, journal = {Informatics}, volume = {5}, number = {2}, year = {2018}, month = {apr}, pages = {21 pages}, abstract = {One of the major problems that social networks face is the continuous production of successful, user-targeted information in the form of recommendations, which are produced exploiting technology from the field of recommender systems. Recommender systems are based on information about users{\textquoteright} past behavior to formulate recommendations about their future actions. However, as time goes by, social network users may change preferences and likings: they may like different types of clothes, listen to different singers or even different genres of music and so on. This phenomenon has been termed as concept drift. In this paper: (1) we establish that when a social network user abstains from rating submission for a long time, it is a strong indication that concept drift has occurred and (2) we present a technique that exploits the abstention interval concept, to drop from the database ratings that do not reflect the current social network user{\textquoteright}s interests, thus improving prediction quality.}, keywords = {collaborative filtering, concept drift, Evaluation, Recommender Systems, shift of interest, Social Networks}, doi = {10.3390/informatics5020021}, url = {http://www.mdpi.com/2227-9709/5/2/21}, author = {Dionisis Margaris and Costas Vassilakis} } @conference {margarisDatacom2018a, title = {Improving Collaborative Filtering{\textquoteright}s Rating Prediction Accuracy by Considering Users{\textquoteright} Rating Variability}, booktitle = {2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech)}, year = {2018}, publisher = {IEEE}, organization = {IEEE}, abstract = {When rating predictions are computed in user-user collaborative filtering, each individual rating is typically adjusted by the mean of the ratings entered by the specific user. This practice takes into account the fact that users follow different rating practices, in the sense that some are stricter when rating items, while others are more lenient. However, users{\textquoteright} rating practices may also differ in rating variability, in the sense that some user may be entering ratings close to her mean, while another user may be entering more extreme ratings, close to the limits of the rating scale. In this work, we (1) propose an algorithm that considers users{\textquoteright} ratings variability in the rating prediction computation process, aiming to improve rating prediction quality and (2) evaluate the proposed algorithm against seven widely used datasets considering three widely used variability measures and two user similarity metrics. The proposed algorithm, using the {\textquotedblleft}mean absolute deviation around the mean{\textquotedblright} variability measure, has been found to intro-duce considerable gains in rating prediction accuracy, in every dataset and under both user similarity metrics tested.}, keywords = {collaborative filtering, Cosine Similarity, Evaluation, Pearson Correlation Coefficient, Users{\textquoteright} Ratings Variability}, doi = {10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00145}, url = {https://ieeexplore.ieee.org/document/8512012}, author = {Dionisis Margaris and Costas Vassilakis} } @conference {margarisDatacom2018b, title = {Improving Collaborative Filtering{\textquoteright}s Rating Prediction Coverage in Sparse Datasets by Exploiting User Dissimilarity}, booktitle = {2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech)}, year = {2018}, month = {aug}, publisher = {IEEE}, organization = {IEEE}, abstract = {Collaborative filtering systems analyze the ratings databases to identify users with similar likings and preferences, termed as near neighbors, and then generate rating predictions for a user by examining the ratings of his near neighbors for items that the user has not yet rated; based on rating predictions, recommenda-tions are then formulated. However, these systems are known to exhibit the {\textquotedblleft}gray sheep{\textquotedblright} problem, i.e. the situation where no near neighbors can be identified for a number of users, and hence no recommendation can be formulated for them. This problem is more intense in sparse datasets, i.e. datasets with relatively small number of ratings, compared to the number of users and items. In this work, we propose a method for alleviating this problem by exploiting user dissimilarity, under the assumption that if some users have exhibited opposing preferences in the past, they are likely to do so in the future. The proposed method has been eval-uated against seven widely used datasets and has been proven to be particularly effective in increasing the percentage of users for which personalized recommendations can be formulated in the context of sparse datasets, while at the same time maintaining or slightly improving rating prediction quality.}, keywords = {collaborative filtering, Evaluation, Negative Correlation, Pearson Correlation Coefficient, Sparse Datasets, User-User Correlation}, doi = {10.1109/dasc/picom/datacom/cyberscitec.2018.00150}, url = {https://ieeexplore.ieee.org/document/8512017}, author = {Dionisis Margaris and Costas Vassilakis} } @article {margaris-tldks, title = {Enhancing User Rating Database Consistency Through Pruning}, journal = {Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXIV}, volume = {LNCS, volume 10620}, number = {Special issue on Consistency and Inconsistency in Data-centric Applications}, year = {2017}, pages = {33{\textendash}64}, publisher = {Springer Berlin Heidelberg}, abstract = {Recommender systems are based on information about users{\textquoteright} past behavior to formulate recommendations about their future actions. However, as time goes by the interests and likings of people may change: people listen to different singers or even different types of music, watch different types of movies, read different types of books and so on. Due to this type of changes, an amount of inconsistency is introduced in the database since a portion of it does not reflect the current preferences of the user, which is its intended purpose. In this paper, we present a pruning technique that removes old aged user behavior data from the ratings database, which are bound to correspond to invalidated preferences of the user. Through pruning (1) inconsistencies are removed and data quality is upgraded, (2) better rating prediction generation times are achieved and (3) the ratings database size is reduced. We also propose an algorithm for determining the amount of pruning that should be performed, allowing the tuning and operation of the pruning algorithm in an unsupervised fashion. The proposed technique is evaluated and compared against seven aging algorithms, which reduce the importance of aged ratings, and a state-of-the-art pruning algorithm, using datasets with varying characteristics. It is also validated using two distinct rating prediction computation strategies, namely collaborative filtering and matrix factorization. The proposed technique needs no extra information concerning the items{\textquoteright} characteristics (e.g. categories that they belong to or attributes{\textquoteright} values), can be used in all rating databases that include a timestamp and has been proved to be effective in any size of users-items database and under two rating prediction computation strategies. Note: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author{\textquoteright}s copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.}, keywords = {Collaborative filtering, Evaluation, Internet of Things, performance, Recommendation Applications, Social Networks, Web services}, doi = {10.1007/978-3-662-55947-5_3}, url = {https://link.springer.com/article/10.1007\%2Fs11761-017-0216-y}, author = {Dionisis Margaris and Costas Vassilakis} } @article {margaris-soca-17, title = {Exploiting Internet of Things information to enhance venues{\textquoteright} recommendation accuracy}, journal = {Service Oriented Computing and Applications}, volume = {todb}, year = {2017}, month = {aug}, pages = {393{\textendash}409}, publisher = {Springer Nature}, abstract = {In this paper, we introduce a novel recommendation algorithm, which exploits data sourced from web services provided by the Internet of Things in order to produce more accurate venue recommendations. The proposed algorithm provides added value for the web services offered by the Internet of Things and enhances the state-of-the-art in this algorithm category by taking into account (a) web of things data regarding the contexts of the user and the context of the venues to be recommended (restaurants, movie theatres, etc.), such as the user{\textquoteright}s geographical position, road traffic and weather conditions, (b) qualitative aspects of the venues, such as price, atmosphere or service, (c) the semantic similarity of venues and (d) the influencing factors between social network users, derived from user participation in social networks. The combination of these features leads to more accurate and better user-targeted recommendations. We also present a framework which incorporates the above characteristics, and we evaluate the presented algorithm, both in terms of performance and recommendation quality.}, keywords = {collaborative filtering, Evaluation, Internet of Things, performance, Recommendation Applications, Social Networks, Web services}, doi = {10.1007/s11761-017-0216-y}, url = {https://link.springer.com/article/10.1007/s11761-017-0216-y}, author = {Dionisis Margaris and Costas Vassilakis} } @conference {margaris-cbi17, title = {Improving Collaborative Filtering{\textquoteright}s Rating Prediction Quality by Considering Shifts in Rating Practices}, booktitle = {Proceedings of the 19th IEEE International Conference on business informatics (CBI17)}, year = {2017}, month = {07}, publisher = {IEEE}, organization = {IEEE}, abstract = {Users that populate ratings databases, such as IMDB, follow different marking practices, in the sense that some are stricter, while others are more lenient. This aspect has been captured by the most widely used similarity metrics in collaborative filtering, namely the Pearson Correlation and the Adjusted Cosine Similarity, which adjust each individual rating by the mean of the ratings entered by the specific user, when computing similarities. However, relying on the mean value presumes that the users{\textquoteright} marking practices remain constant over time; in practice though, it is possible that a user{\textquoteright}s marking practices change over time, i.e. a user could start as strict and subsequently become lenient, or vice versa. In this work, we propose an approach to take into account marking practices shifts by (1) introducing the concept of dynamic user rating averages which follow the users{\textquoteright} marking practices shifts, (2) presenting two alternative algorithms for computing a user{\textquoteright}s dynamic averages and (3) performing a comparative evaluation among these two algorithms and the classic static average (unique mean value) that the Pearson Correlation uses.}, doi = {10.1109/CBI.2017.24}, url = {https://ieeexplore.ieee.org/abstract/document/8010718/}, author = {Dionisis Margaris and Costas Vassilakis} } @conference {margaris-iscc17, title = {Improving Collaborative Filtering{\textquoteright}s Rating Prediction Quality in Dense Datasets, by Pruning Old Ratings}, booktitle = {Proceedings of the 22nd IEEE Symposium on Computers and Communications (ISCC17)}, year = {2017}, month = {07}, publisher = {IEEE}, organization = {IEEE}, abstract = {In this paper, we introduce a pruning algorithm which removes aged user ratings from the rating database used by collaborative filtering algorithms, in order to (1) improve prediction quality and (2) minimize the rating database size, as well as the rating prediction generation time. The proposed algorithm needs no extra information concerning the items{\textquoteright} characteristics (e.g. categories that they belong to or attributes{\textquoteright} values) and can be used with all rating databases that include a timestamp. Furthermore, we propose and validate a method for identifying the most prominent combination of a pruning algorithm and a pruning level for datasets, allowing thus to perform the selection of pruning algorithm and pruning level in an unsupervised fashion.}, doi = {10.1109/ISCC.2017.8024683}, url = {https://ieeexplore.ieee.org/document/8024683/}, author = {Dionisis Margaris and Costas Vassilakis} } @inbook {marg16d, title = {Knowledge-Based Leisure Time Recommendations in Social Networks}, booktitle = {Current Trends on Knowledge-Based Systems}, series = {Intelligent Systems Reference Library}, year = {2017}, pages = {23{\textendash}48}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, chapter = {Knowledge-Based Leisure Time Recommendations in Social Networks}, abstract = {We introduce a novel knowledge-based recommendation algorithm for leisure time information to be used in social networks, which enhances the state-of-the-art in this algorithm category by taking into account (a) qualitative aspects of the recommended places (restaurants, museums, tourist attractions etc.), such as price, service and atmosphere, (b) influencing factors between social network users, (c) the semantic and geographical distance between locations and (d) the semantic categorization of the places to be recommended. The combination of these features leads to more accurate and better user-targeted leisure time recommendations.}, keywords = {Attribute constraints, collaborative filtering, Knowledge-based Recommender Systems, Semantic Information, Social Networks}, doi = {10.1007/978-3-319-51905-0_2}, author = {Dionisis Margaris and Costas Vassilakis and P. Georgiadis}, editor = {Giner Alor Hernandez and Rafael Valencia Garcia} } @article {DBLP:journals/tkde/TerrovitisPMS17, title = {Local Suppression and Splitting Techniques for Privacy Preserving Publication of Trajectories}, journal = {IEEE Trans. Knowl. Data Eng.}, volume = {29}, number = {7}, year = {2017}, pages = {1466{\textendash}1479}, doi = {10.1109/TKDE.2017.2675420}, url = {https://doi.org/10.1109/TKDE.2017.2675420}, author = {Manolis Terrovitis and Giorgos Poulis and Nikos Mamoulis and Spiros Skiadopoulos} } @conference {boukoros2017, title = {Modeling Server Workloads for Campus Email Traffic Using Recurrent Neural Networks}, booktitle = {Proceedings of the 24th International Conference on Neural Information Processing (ICONIP 2017)}, year = {2017}, pages = {57{\textendash}66}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, abstract = {As email workloads keep rising, email servers need to handle this explosive growth while offering good quality of service to users. In this work, we focus on modeling the workload of the email servers of four universities (2 from Greece, 1 from the UK, 1 from Australia). We model all types of email traffic, including us-er and system emails, as well as spam. We initially tested some of the most popu-lar distributions for workload characterization and used statistical tests to evaluate our findings. The significant differences in the prediction accuracy results for the four datasets led us to investigate the use of a Recurrent Neural Network (RNN) as time series modeling to model the server workload, which is a first for such a problem. Our results show that the use of RNN modeling leads in most cases to high modeling accuracy for all four campus email traffic datasets.}, keywords = {Email traffic, Model server workload, Recurrent neural network, Time series modeling}, doi = {10.1007/978-3-319-70139-4_6}, author = {Spyros Boukoros and Anupiya Nugaliyadde and Angelos Marnerides and Costas Vassilakis and Polychronis Koutsakis and Kok Wai Wong} } @conference {theodorakopoulos-smap2017, title = {Personalized Augmented Reality Experiences in Museums using Google Cardboards}, booktitle = {2017 12th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP)}, year = {2017}, month = {07}, publisher = {IEEE CPS}, organization = {IEEE CPS}, abstract = {In this short paper we examine the suitability of the Google Cardboard as a means for the delivery of personalized cultural experiences. Specifically, we develop the content and create the application required in order to provide highly personalized visits to the Archaeological Museum in Tripolis, Greece. We also examine the usability issues related to the use of Google Cardboards. Early results are promising, and based on them we also outline the next steps ahead.}, doi = {10.1109/smap.2017.8022676}, url = {https://ieeexplore.ieee.org/document/8022676/}, author = {Marinos Theodorakopoulos and Nikos Papageorgopoulos and Andriana Mourti and Angela Antoniou and Manolis Wallace and Giorgos Lepouras and Costas Vassilakis and Nikos Platis} } @article {marg2017a-fgcs, title = {Query personalization using social network information and collaborative filtering techniques}, journal = {Future Generation of Computer Systems}, volume = {Special Issue on Recommender Systems for Large-Scale Social Networks}, year = {2017}, month = {jan}, pages = {440{\textendash}450}, abstract = {Query personalization has emerged as a means to handle the issue of information volume growth, aiming to tailor query answer results to match the goals and interests of each user. Query personalization dynamically enhances queries, based on information regarding user preferences or other contextual information; typically enhancements relate to incorporation of conditions that filter out results that are deemed of low value to the user and/or ordering results so that data of high value are presented first. In the domain of personalization, social network information can prove valuable; users{\textquoteright} social networks profiles, including their interests, influence from social friends, etc. can be exploited to personalize queries. In this paper, we present a query personalization algorithm, which employs collaborative filtering techniques and takes into account influence factors between social network users, leading to personalized results that are better-targeted to the user.}, doi = {10.1016/j.future.2017.03.015}, url = {http://www.sciencedirect.com/science/article/pii/S0167739X17303692}, author = {Dionisis Margaris and Costas Vassilakis and P. Georgiadis} } @article {margaris-csimq-2017, title = {Using Time Clusters for Following Users{\textquoteright} Shifts in Rating Practices}, journal = {Complex Systems Informatics and Modeling Quarterly}, volume = {13}, year = {2017}, abstract = {Users that enter ratings for items follow different rating practices, in the sense that, when rating items, some users are more lenient, while others are stricter. This aspect is taken into account by the most widely used similarity metric in user-user collaborative filtering, namely, the Pearson Correlation, which adjusts each individual user rating by the mean value of the ratings entered by the specific user, when computing similarities. However, a user{\textquoteright}s rating practices change over time, i.e. a user could start as strict and subsequently become lenient or vice versa. In that sense, the practice of using a single mean value for adjusting users{\textquoteright} ratings is inadequate, since it fails to follow such shifts in users{\textquoteright} rating practices, leading to decreased rating prediction accuracy. In this work, we address this issue by using the concept of dynamic averages introduced earlier and we extend earlier work by (1) introducing the concept of rating time clusters and (2) presenting a novel algorithm for calculating dynamic user averages and exploiting them in user-user collaborative, filtering implementations. The proposed algorithm incorporates the aforementioned concept and is able to follow more successfully shifts in users{\textquoteright} rating practices. It has been evaluated using numerous datasets, and has been found to introduce significant gains in rating prediction accuracy, while outperforming the dynamic average computation approaches that are presented earlier.}, keywords = {collaborative filtering, Dynamic Average, Rating Abstention Interval, Rating Time Clusters, Ratings{\textquoteright} Timestamps, Recommender Systems}, issn = {2255-9922}, doi = {10.7250/csimq.2017-13.02}, author = {Dionisis Margaris and Costas Vassilakis} } @conference {margaris2016a, title = {Improving QoS Delivered by WS-BPEL Scenario Adaptation through Service Execution Parallelization}, booktitle = {Proceedings of the 31st Annual ACM Symposium on Applied Computing}, year = {2016}, pages = {1590-1596}, publisher = {ACM}, organization = {ACM}, abstract = {WS-BPEL scenario execution adaptation has been proposed by researchers as a response to the need of users to tailor the WS-BPEL scenario execution to their individual preferences; these preferences are typically expressed through Quality of Service (QoS) policies, which the adaptation mechanism considers in order to select the services that will ultimately be invoked to realize the desired business process. In this paper, we study the potential to parallelize the execution of the WS-BPEL scenario in order to minimize its response time and/or achieving higher scores in the other qualitative dimensions, such as cost, reliability, etc., at the same time. We also describe, develop and validate a parallelization algorithm for realizing the proposed enhancements.}, doi = {10.1145/2851613.2851805}, url = {https://dl.acm.org/citation.cfm?id=2851805}, author = {Dionisis Margaris and Costas Vassilakis and P. Georgiadis} } @conference {marg2016-ssci, title = {Pruning and Aging for User Histories in Collaborative Filtering}, booktitle = {Proceedings of the 2016 IEEE Symposium Series on Computational Intelligence}, year = {2016}, month = {12}, abstract = {In this paper, we introduce algorithms for pruning and aging user ratings in collaborative filtering systems, based on their oldness, under the rationale that aged user ratings may not accurately reflect the current state of users regarding their preferences. The aging algorithm reduces the importance of aged ratings, while the pruning algorithm removes them from the database. The algorithms are evaluated against various types of datasets. The pruning algorithm has been found to present a number of advantages, namely (1) reducing the rating database size, (2) achieving better prediction generation times and (3) improving prediction quality by cutting off predictions with high error. The algorithm can be used in all rating databases that include a timestamp and has been proved to be effective in any type of dataset, from movies and music, to videogames and books.}, doi = {10.1109/ssci.2016.7849920}, url = {https://ieeexplore.ieee.org/document/7849920/}, author = {Dionisis Margaris and Costas Vassilakis} } @article {Margaris_2016, title = {Recommendation information diffusion in social networks considering user influence and semantics}, journal = {Social Network Analysis and Mining}, volume = {6}, number = {1}, year = {2016}, month = {nov}, pages = {108}, publisher = {Springer Nature}, abstract = {One of the major problems in the domain of social networks is the handling and diffusion of the vast, dynamic and disparate information created by its users. In this context, the information contributed by users can be exploited to generate recommendations for other users. Relevant recommender systems take into account static data from users{\textquoteright} profiles, such as location, age or gender, complemented with dynamic aspects stemming from the user behavior and/or social network state such as user preferences, items{\textquoteright} general acceptance and influence from social friends. In this paper, we enhance recommendation algorithms used in social networks by taking into account qualitative aspects of the recommended items, such as price and reliability, the influencing factors between social network users, the social network user behavior regarding their purchases in different item categories and the semantic categorization of the products to be recommended. The inclusion of these aspects leads to more accurate recommendations and diffusion of better user-targeted information. This allows for better exploitation of the limited recommendation space, and therefore online advertisement efficiency is raised.}, keywords = {Collaborative filtering, Information diffusion, Quality of service, Semantic Information, Social Networks}, doi = {10.1007/s13278-016-0416-z}, url = {http://rdcu.be/mZ7e}, author = {Dionisis Margaris and Costas Vassilakis and P. Georgiadis} } @conference {575, title = {R-Susceptibility: An IR-Centric Approach to Assessing Privacy Risks for Users in Online Communities}, booktitle = {Proceedings of the 39th Annual International ACM SIGIR Conference}, year = {2016}, month = {July}, address = {Pisa, Italy}, author = {Joanna Asia Biega and Krishna P. Gummadi and Ida Mele and Dragan Milchevski and Christos Tryfonopoulos and Gerhard Weikum} } @conference {marg15a, title = {A Collaborative Filtering Algorithm with Clustering for Personalized Web Service Selection in Business Processes}, booktitle = {Proceedings of RCIS 2015}, year = {2015}, month = {5}, abstract = {Recommender systems aim to propose items that are expected to be of interest to the users. As one of the most successful approaches to building recommender systems, collaborative filtering exploits the known preferences of a group of users to formulate recommendations or predictions of the unknown preferences for other users. In many cases, collaborative filtering algorithms handle complex items, which are described using hierarchical tree structures containing rich semantic information. In order to make accurate recommendations on such items, the related algorithms must examine all aspects of the available semantic information. Thus, when collaborative filtering techniques are employed to adapt the execution of business processes, they must take into account the services{\textquoteright} Quality of Service parameters, so as to generate recommendations tailored to the individual user needs. In this paper, we present a collaborative filtering-based algorithm which takes into account the web services{\textquoteright} QoS parameters in order to tailor the execution of business processes to the preferences of users. An offline clustering technique is also introduced for supporting the efficient and scalable execution of proposed algorithm under the presence of large repositories of sparse data.}, keywords = {business processes, clustering, collaborative filtering, hierarchical tree, performance, Quality of service, Web services}, doi = {10.1109/RCIS.2015.7128877}, author = {Dionisis Margaris and P. Georgiadis and Costas Vassilakis} } @conference {margaris2015a, title = {A Hybrid Framework for WS-BPEL Scenario Execution Adaptation, Using Monitoring and Feedback Data}, booktitle = {Proceedings of the 30th ACM/SIGAPP Symposium On Applied Computing, SOAP track}, year = {2015}, month = {4}, publisher = {ACM}, organization = {ACM}, abstract = {In this paper, we present a framework which provides runtime adaptation for BPEL scenarios. The adaptation is based on (a) quality of service parameters of available web services (b) quality of service policies specified by users (c) collaborative filtering techniques, allowing clients to further refine the adaptation process by considering service selections made by other clients, (d) monitoring, in order to follow the variations of QoS attribute values and (e) on users{\textquoteright} opinions services they have used.}, doi = {10.1145/2695664.2695687}, url = {https://dl.acm.org/citation.cfm?doid=2695664.2695687}, author = {Dionisis Margaris and Costas Vassilakis and P. Georgiadis} } @article {margaris14a, title = {An integrated framework for adapting WS-BPEL scenario execution using QoS and collaborative filtering techniques}, journal = {Science of Computer Programming}, volume = {98}, year = {2015}, pages = {707{\textendash}734}, abstract = {In this paper, we present a framework which incorporates runtime adaptation for BPEL scenarios. The adaptation is based on (a) the quality of service parameters of available services, allowing for tailoring their execution to the diverse needs of individual users and (b)collaborative filtering techniques, allowing clients to further refine the adaptation process by considering service selections made by other clients, in the context of the same business processes. The proposed framework also caters maintaining the transactional semantics that invocations to multiple services offered by the same provider may bear.}, doi = {10.1016/j.scico.2014.10.007}, url = {http://www.sciencedirect.com/science/article/pii/S0167642314004778}, author = {Dionisis Margaris and Costas Vassilakis and P. Georgiadis} } @conference {556, title = {A Platform for the Presentation and Dissemination of Cultural Heritage Information for Mobile Devices Enriched with Augmented Reality}, booktitle = {Proceedings of the Greek Conference for the Digitization of Cultural Information (EUROMED)}, year = {2015}, month = {September}, address = {Volos, Greece}, author = {K. Masselos and Costas Vassilakis and Giorgos Lepouras and Christos Tryfonopoulos and N. Tselikas and N. Platis} } @conference {margaris15c, title = {On Replacement Service Selection in WS-BPEL Scenario Adaptation}, booktitle = {Proceedings of the 8th IEEE International Conference on Service Oriented Computing \& Applications (SOCA 2015)}, year = {2015}, month = {9}, publisher = {IEEE}, organization = {IEEE}, abstract = {WS-BPEL scenario execution adaptation has been proposed by numerous researchers as a response to the need of users to tailor the WS-BPEL scenario execution to their individual preferences; these preferences are typically expressed through Quality of Service (QoS) policies, which the adaptation mechanism considers in order to select the services that will ultimately be invoked to realize the desired business process. In this paper, we consider a number of issues related to WS-BPEL scenario adaptation, aiming to enhance adaptation quality and improve the QoS offered to end users. More specifically, with the goal of broadening the service selection pool we (a) discuss the identification of potential services that can be used to realize a functionality used in the WS-BPEL scenario and (b) elaborate on transactional semantics that invocations to multiple services offered by the same provider may bear. We also describe and validate an architecture for realizing the proposed enhancements.}, doi = {10.1109/SOCA.2015.11}, author = {Dionisis Margaris and P. Georgiadis and Costas Vassilakis} } @conference {Margaris2013RCIS, title = {Adapting WS-BPEL scenario execution using collaborative filtering techniques}, booktitle = {IEEE 7th International Conference on Research Challenges in Information Science (RCIS)}, year = {2013}, month = {may}, publisher = {IEEE}, organization = {IEEE}, abstract = {WS-BPEL has been adopted as the predominant method for composing individual web services into higher-level business processes. The designers of WS-BPEL scenarios define at development time the specific web services that will be invoked in the context of the business process they model; in the context however of the current web, where each functionality is offered by multiple service providers, under different quality of service parameters, using a fixed BPEL scenario has been recognized to be inadequate for servicing the diverse needs of business processes clients. To this end, WS-BPEL scenario execution adaptation has been proposed, mainly allowing clients to specify quality of service policies, which drive the dynamic selection of the services that will be invoked. In this paper, we present a framework extending the quality of service-based adaptation mechanisms with collaborative filtering techniques, allowing clients to further refine the adaptation process by considering service selections made by other clients, in the context of the same business processes.}, doi = {10.1109/rcis.2013.6577691}, author = {Dionisis Margaris and P. Georgiadis and Costas Vassilakis} } @conference {Margaris2013SAC, title = {An integrated framework for QoS-based adaptation and exception resolution in WS-BPEL scenarios}, booktitle = {Proceedings of the 28th Annual ACM Symposium on Applied Computing - SAC \textquotesingle13}, year = {2013}, month = {3}, publisher = {ACM Press}, organization = {ACM Press}, abstract = {In this paper, we present a framework which incorporates runtime quality of service-based adaptation for BPEL scenarios, allowing for tailoring their execution to the diverse needs of individual users. The proposed framework also caters for automatically resolving system-level exceptions, such as machine outages or network partitionings, while both scenario execution adaptation and exception resolution maintain the transactional semantics that invocations to multiple services offered by the same provider may bear.}, doi = {10.1145/2480362.2480714}, author = {Dionisis Margaris and Costas Vassilakis and P. Georgiadis} } @article {DBLP:journals/corr/abs-1207-0135, title = {Privacy Preservation by Disassociation}, journal = {CoRR}, volume = {abs/1207.0135}, year = {2012}, url = {http://arxiv.org/abs/1207.0135}, author = {Manolis Terrovitis and John Liagouris and Nikos Mamoulis and Spiros Skiadopoulos} } @article {DBLP:journals/pvldb/TerrovitisLMS12, title = {Privacy Preservation by Disassociation}, journal = {PVLDB}, volume = {5}, number = {10}, year = {2012}, pages = {944{\textendash}955}, doi = {10.14778/2336664.2336668}, url = {http://vldb.org/pvldb/vol5/p944_manolisterrovitis_vldb2012.pdf}, author = {Manolis Terrovitis and John Liagouris and Nikos Mamoulis and Spiros Skiadopoulos} } @article {DBLP:journals/pvldb/MansourASK11, title = {ERA: Efficient Serial and Parallel Suffix Tree Construction for Very Long Strings}, journal = {PVLDB}, volume = {5}, number = {1}, year = {2011}, pages = {49{\textendash}60}, doi = {10.14778/2047485.2047490}, url = {http://www.vldb.org/pvldb/vol5/p049_essammansour_vldb2012.pdf}, author = {Essam Mansour and Amin Allam and Spiros Skiadopoulos and Panos Kalnis} } @article {DBLP:journals/corr/abs-1109-6884, title = {ERA: Efficient Serial and Parallel Suffix Tree Construction for Very Long Strings}, journal = {CoRR}, volume = {abs/1109.6884}, year = {2011}, url = {http://arxiv.org/abs/1109.6884}, author = {Essam Mansour and Amin Allam and Spiros Skiadopoulos and Panos Kalnis} } @article {dix2010b, title = {From the web of data to a world of action}, journal = {Journal of Web Semantics}, volume = {8 issue 4}, year = {2010}, pages = {394-408}, abstract = {This paper takes as its premise that the web is a place of action, not just information, and that the purpose of global data is to serve human needs. The paper presents several component technologies, which together work towards a vision where many small micro-applications can be threaded together using automated assistance to enable a unified and rich interaction. These technologies include data detector technology to enable any text to become a start point of semantic interaction; annotations for web-based services so that they can link data to potential actions; spreading activation over personal ontologies, to allow modelling of context; algorithms for automatically inferring {\textquoteleft}typing{\textquoteright} of web-form input data based on previous user inputs; and early work on inferring task structures from action traces. Some of these have already been integrated within an experimental web-based (extended) bookmarking tool, Snip!t, and a prototype desktop application On Time, and the paper discusses how the components could be more fully, yet more openly, linked in terms of both architecture and interaction. As well as contributing to the goal of an action and activity-focused web, the work also exposes a number of broader issues, theoretical, practical, social and economic, for the Semantic Web.}, doi = {10.1016/j.websem.2010.04.007}, author = {Alan Dix and Giorgos Lepouras and Akrivi Katifori and Costas Vassilakis and Tiziana Catarci and Antonella Poggi and Yannis Ioannidis and Miguel Morae and I. Daradimos and Nazihah Md.Akima and Shah Rukh Humayound and Fabio Terella} } @conference {537, title = {Anonymity and Censorship Resistance in Unstructured Overlay Networks}, booktitle = {Proceedings of the 17th International Conference on Cooperative Information Systems (CoopIS)}, year = {2009}, month = {November}, address = {Vilamoura, Portugal}, author = {Michael Backes and Marek Hamerlik and Alessandro Linari and Matteo Maffei and Christos Tryfonopoulos and Gerhard Weikum} } @booklet {569, title = {Anonymous and Censorship-resistant Content-sharing in Unstructured Overlays}, year = {2008}, author = {Michael Backes and Marek Hamerlik and Alessandro Linari and Matteo Maffei and Christos Tryfonopoulos and Gerhard Weikum} } @conference {Mateevitsi2008451, title = {A game-engine based virtual museum authoring and presentation system}, booktitle = {Proceedings - 3rd International Conference on Digital Interactive Media in Entertainment and Arts, DIMEA 2008}, year = {2008}, note = {cited By (since 1996) 0}, month = {9}, pages = {451-457}, abstract = {In this paper we present a system that facilitates virtual museum development and usage. The system is based on a game engine, ensuring thus minimal cost and good performance, and includes provisions that enable museum curators design the virtual museum without any specialized knowledge. Besides visual and auditory information, museum curators may also provide metadata which provide additional information to the visitor, while they can be also exploited for searching for exhibits with certain properties. A guide is also included in the museum, to present additional information to the visitors and aid them throughout their tour. Copyright 2008 ACM.}, keywords = {Authoring environment, Game engines, Game theory, Metadata, Minimal costs, Museum guide, Presentation systems, Specialized knowledge, Virtual museum}, doi = {10.1145/1413634.1413714}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-62849090231\&partnerID=40\&md5=2ba9707b8125ec8f1cacaad99b611342}, author = {Mateevitsi, V. and Sfakianos, M. and Giorgos Lepouras and Costas Vassilakis} } @inbook {gol2009a, title = {Intelligent User Interfaces: Adaptation and Personalization Systems and Technologies}, year = {2008}, pages = {188{\textendash}204}, publisher = {IGI Publishing}, organization = {IGI Publishing}, chapter = {Context and Adaptivity-Driven Visualization Method Selection}, abstract = {Novel and intelligent visualization methods are being developed in order to accommodate user searching and browsing tasks, including new and advanced functionalities. Besides, research in the field of user modeling is progressing in order to personalize these visualization systems, according to its users{\textquoteright} individual profiles. However, employing a single visualization system, may not suit best any information seeking activity. In this paper we present a visualization environment, which is based on a visualization library, i.e. is a set of visualization methods, from which the most appropriate one is selected for presenting information to the user. This selection is performed combining information extracted from the context of the user, the system configuration and the data collection. A set of rules inputs such information and assigns a score to all candidate visualization methods. The presented environment additionally monitors user behavior and preferences to adapt the visualization method selection criteria.}, author = {M. Golemati and Costas Vassilakis and Akrivi Katifori and Giorgos Lepouras and C. Halatsis}, editor = {C. Mourlas and P. Germanakos} } @conference {Vasiliadis200890, title = {Performance Evaluation of Distance Vector Routing Protocol on a Wireless Circular Model}, booktitle = {Proceedings of the IEEE sponsored International Joint Conference CISSE 2007}, year = {2008}, month = {12}, pages = {323-328}, publisher = {Springer Netherlands}, organization = {Springer Netherlands}, abstract = {In this paper, a wireless Circular Model over a generic distance-vector routing protocol is presented and analyzed. The performance of this model over the Distance Vector protocol, which is an implementation of Distributed Bellman-Ford algorithm, has been evaluated by using the simulation environment of NS-2. We conducted an extensive evaluation study for various mobility schemes in order to incorporate the behavior of nodes and the routing protocol in a real-life hotspot situation In the test-bed model, while the number of source nodes was allowed to arbitrarily vary, there was exactly one destination node, closely modeling thus real-life situations where a single hotspot/access point exists. Finally, different constant bit rates (CBR) were used in order to estimate the throughput of receiving, dropping rates, the number of lost packets, as well as the average packet delay under various traffic conditions. This study is aimed to help wireless network designers in choosing the best suited routing protocols for their networks, through making explicit performance figures for common network setups.}, doi = {10.1007/978-1-4020-8737-0}, author = {D.C. Vasiliadis and G.E. Rizos and Costas Vassilakis}, editor = {Tarek Sobh and Khaled Elleithy and Ausif Mahmood and Mohammad A. Karim} } @inbook {vas2007a, title = {Encyclopedia of Digital Government}, year = {2007}, pages = {1174{\textendash}1179}, publisher = {IGI Publishing}, organization = {IGI Publishing}, chapter = {The Lifecycle Of Transactional Services}, abstract = {Transactional services are an indispensible part of e-government, since provision of services to citizens and enterprises,as well as the interaction between the government and citizens-enterprises are modelled mainly through such services. Latest quantifications, however, show that the development of such services lags behind as compared both to the expectations of citizens-enterprises and to the efforts made by governments. This can be attributed, amongst other reasons, to the "traditional" approach to electronic service development, which treats each electronic service as an isolated software project. In this paper, we propose an e-service development platform, which covers the whole lifecycle of transactional services and facilitates the analysis, development, deployment and maintenance of these services.}, doi = {10.4018/978-1-59140-789-8.ch177}, author = {Costas Vassilakis and Giorgos Lepouras}, editor = {Ari-Veikko Anttiroiko and Matti Malkia} } @inbook {lep2007a, title = {Encyclopedia of Digital Government}, year = {2007}, pages = {1413{\textendash}1417}, publisher = {IGI Publishing}, organization = {IGI Publishing}, chapter = {Reusability In Governmental Electronic Services}, abstract = {Reusability is the degree to which a software component or other work can be used in more than one programs or software systems. e-Government is a prosperous area for the application of reusability, since the services offered to citizens from the same administration, or even different administrations, have common portions that can be developed only once and reused wherever appropriate. In this paper we present the design and implementation of an electronic service development environment which offers the potential to reuse components that have already been implemented, for the realization of new services.}, doi = {10.4018/978-1-59140-789-8.ch216}, author = {Giorgos Lepouras and Costas Vassilakis}, editor = {Ari-Veikko Anttiroiko and Matti Malkia} } @article {DBLP:journals/dke/TerrovitisVSBCMR07, title = {Modeling and language support for the management of pattern-bases}, journal = {Data Knowl. Eng.}, volume = {62}, number = {2}, year = {2007}, pages = {368{\textendash}397}, doi = {10.1016/j.datak.2006.10.002}, url = {https://doi.org/10.1016/j.datak.2006.10.002}, author = {Manolis Terrovitis and Panos Vassiliadis and Spiros Skiadopoulos and Elisa Bertino and Barbara Catania and Anna Maddalena and Stefano Rizzi} } @article {Magoulas200771, title = {Virtual reality in the e-Society}, journal = {Virtual Reality}, volume = {11}, number = {2-3}, year = {2006}, note = {cited By (since 1996) 1}, month = {dec}, pages = {71-73}, publisher = {Springer Nature}, abstract = {This special issue explores the extent to which virtual reality (VR) is affecting the creation of an electronic society. E-Society is a broad term used to describe a research area covering aspects of digital technologies for large user communities. Recent years have seen the emergence of various electronic services in an attempt to facilitate everyday life and improve the way common tasks are being carried out. The term e-Society covers a wide range of applications from e-government, e-democracy, and e-business to e-learning and e-health. In order for VR to contribute to the creation and advancement of e-Society, a number of issues have to be tackled. A successful VR system has to find a balance between the hardware requirements, user interaction methods, content presentation and the effort required for development and maintenance. Hardware requirements define to a large degree the extent to which an end-user can afford to execute the VR system at her home. User interaction methods have to cater for the variety of users? needs. Overall, design and implementation of a successful and engaging VR system is a rather difficult and complex task which requires increased effort in human power and resources in comparison to typical window based applications. Flexibility in development and subsequently maintenance of such a system can be achieved by adopting techniques already present in rapid application development environments, like abstraction, automatic code generation and reusability.}, doi = {10.1007/s10055-006-0064-0}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-34347395236\&partnerID=40\&md5=88157a2c66f3799e7bcfaf75eda9548a}, author = {G. D. Magoulas and Giorgos Lepouras and Costas Vassilakis} } @inbook {lepbook2005a, title = {Adaptable and Adaptive Hypermedia Systems}, year = {2005}, pages = {190{\textendash}205}, publisher = {IRM Press}, organization = {IRM Press}, chapter = {Adaptive Virtual Museums on the Web}, abstract = {This chapter presents an architecture for supporting the creation of adaptive virtual reality museums on the web. It argues whether the task of developing adaptive virtual reality museums is a complex one, presenting key challenges, and should thus be facilitated by means of a supporting architecture and relevant tools. The proposed architecture is flexible enough to cater for a variety of user needs, and modular promoting extensibility, maintainability and tailorability. Adoption of this architecture will greatly simplify the development of adaptive virtual reality museums, reducing the needed effort to exhibit digitisation and user profile specification; user profiles are further refined dynamically through the user data recorder and the user modelling engine, which provide input for the virtual environment generator.}, doi = {10.4018/978-1-59140-567-2.ch010}, author = {Giorgos Lepouras and Costas Vassilakis}, editor = {S. Y. Chen and G. D. Magoulas} } @proceedings {DBLP:conf/er/2005, title = {Conceptual Modeling - ER 2005, 24th International Conference on Conceptual Modeling, Klagenfurt, Austria, October 24-28, 2005, Proceedings}, volume = {3716}, year = {2005}, publisher = {Springer}, isbn = {3-540-29389-2}, doi = {10.1007/11568322}, url = {https://doi.org/10.1007/11568322}, editor = {Lois M. L. Delcambre and Christian Kop and Heinrich C. Mayr and John Mylopoulos and Oscar Pastor} } @article {DBLP:journals/jdwm/ManiatisVSVMM05, title = {A Presentation Model \& Non-Traditional Visualization for OLAP}, journal = {IJDWM}, volume = {1}, number = {1}, year = {2005}, pages = {1{\textendash}36}, doi = {10.4018/jdwm.2005010101}, url = {https://doi.org/10.4018/jdwm.2005010101}, author = {Andreas S. Maniatis and Panos Vassiliadis and Spiros Skiadopoulos and Yannis Vassiliou and George Mavrogonatos and Ilias Michalarias} } @conference {598, title = {Semantic Similarity Methods in WordNet and their Application to Information Retrieval on the Web}, booktitle = {Proceedings of 7th ACM International Workshop on Web Information and Data Management (WIDM)}, year = {2005}, month = {November}, publisher = {ACM}, organization = {ACM}, address = {Bremen, Germany}, abstract = {Semantic Similarity relates to computing the similarity be- tween concepts which are not lexicographically similar. We investigate approaches to computing semantic similarity by mapping terms (concepts) to an ontology and by examining their relationships in that ontology. Some of the most popular semantic similarity methods are implemented and evaluated using WordNet as the underlying reference ontology. Building upon the idea of semantic similarity, a novel information retrieval method is also proposed. This method is capable of detecting similarities between documents containing semantically similar but not necessarily lexicographically similar terms. The proposed method has been evaluated in retrieval of images and documents on the Web. The experimental results demonstrated very promising performance improvements over state-of-the-art information retrieval methods.}, keywords = {Information retrieval, Semantic Similarity, WordNet, World Wide Web}, author = {Giannis Varelas and Epimenides Voutsakis and Paraskevi Raftopoulou and E.G.M. Petrakis and Evangelos Milios} } @conference {DBLP:conf/ssdbm/TerrovitisVSBCM04, title = {Modeling and Language Support for the Management of Pattern-Bases}, booktitle = {Proceedings of the 16th International Conference on Scientific and Statistical Database Management (SSDBM 2004), 21-23 June 2004, Santorini Island, Greece}, year = {2004}, pages = {265{\textendash}274}, doi = {10.1109/SSDBM.2004.54}, url = {http://doi.ieeecomputersociety.org/10.1109/SSDBM.2004.54}, author = {Manolis Terrovitis and Panos Vassiliadis and Spiros Skiadopoulos and Elisa Bertino and Barbara Catania and Anna Maddalena} } @article {Maroulis2004267, title = {A web browser for ISDN card phones}, journal = {Journal of Internet Technology}, volume = {5}, number = {3}, year = {2004}, note = {cited By (since 1996) 1}, pages = {267-277}, abstract = {In order to cover the ever-increasing need for more direct and easy access to information, new information access means need to be devised or existing ones need to be further exploited. In this paper, we present a mini Web Browser for ISDN card phones, which enables this widespread device to be used for accessing information in the World Wide Web. The implemented web browser supports HTML and WML pages, while special care was taken to tackle the limitations imposed by the ISDN card phone{\textquoteright}s hardware, such as small screen, limited keyboard, scarce processing and memory resources. One of the techniques employed to increase the capabilities of the ISDN card phone browser was the introduction of a proxy server, which transforms demanding media types to formats that can be handled by the ISDN card phone hardware.}, keywords = {Bandwidth, Costs, Embedded systems, HTML, Information analysis, Integrated multiprotocol, ISDN card phone, Laptop computers, Mobile phones, Personal computers, Personal digital assistants, Proxy servers, Voice/data communication systems, Web browsers}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-4344632311\&partnerID=40\&md5=9242e761d5eedb61880ca004cdaa873d}, author = {Maroulis, D. and Aronis, S. and Nassiapoulou, V. and Grammenos, N. and Costas Vassilakis} } @conference {DBLP:conf/dolap/ManiatisVSV03, title = {Advanced visualization for OLAP}, booktitle = {DOLAP 2003, ACM Sixth International Workshop on Data Warehousing and OLAP, New Orleans, Louisiana, USA, November 7, 2003, Proceedings}, year = {2003}, pages = {9{\textendash}16}, doi = {10.1145/956060.956063}, url = {https://doi.org/10.1145/956060.956063}, author = {Andreas S. Maniatis and Panos Vassiliadis and Spiros Skiadopoulos and Yannis Vassiliou} } @conference {DBLP:conf/dawak/ManiatisVSV03, title = {CPM: A Cube Presentation Model for OLAP}, booktitle = {Data Warehousing and Knowledge Discovery, 5th International Conference, DaWaK 2003, Prague, Czech Republic, September 3-5,2003, Proceedings}, year = {2003}, pages = {4{\textendash}13}, doi = {10.1007/978-3-540-45228-7_2}, url = {https://doi.org/10.1007/978-3-540-45228-7_2}, author = {Andreas S. Maniatis and Panos Vassiliadis and Spiros Skiadopoulos and Yannis Vassiliou} } @proceedings {DBLP:conf/dawak/2003, title = {Data Warehousing and Knowledge Discovery, 5th International Conference, DaWaK 2003, Prague, Czech Republic, September 3-5,2003, Proceedings}, volume = {2737}, year = {2003}, publisher = {Springer}, isbn = {3-540-40807-X}, doi = {10.1007/b11825}, url = {https://doi.org/10.1007/b11825}, editor = {Yahiko Kambayashi and Mukesh K. Mohania and Wolfram W{\"o}{\ss}} } @proceedings {DBLP:conf/chorochronos/2003, title = {Spatio-Temporal Databases: The CHOROCHRONOS Approach}, volume = {2520}, year = {2003}, publisher = {Springer}, isbn = {3-540-40552-6}, doi = {10.1007/b83622}, url = {https://doi.org/10.1007/b83622}, editor = {Manolis Koubarakis and Timos K. Sellis and Andrew U. Frank and St{\'e}phane Grumbach and Ralf Hartmut G{\"u}ting and Christian S. Jensen and Nikos A. Lorentzos and Yannis Manolopoulos and Enrico Nardelli and Barbara Pernici and Hans-J{\"o}rg Schek and Michel Scholl and Babis Theodoulidis and Nectaria Tryfona} } @proceedings {DBLP:conf/caise/2002, title = {Advanced Information Systems Engineering, 14th International Conference, CAiSE 2002, Toronto, Canada, May 27-31, 2002, Proceedings}, volume = {2348}, year = {2002}, publisher = {Springer}, isbn = {3-540-43738-X}, doi = {10.1007/3-540-47961-9}, url = {https://doi.org/10.1007/3-540-47961-9}, editor = {Anne Banks Pidduck and John Mylopoulos and Carson C. Woo and M. Tamer {\"O}zsu} } @conference {Georgiadis_2002, title = {SmartGov: A Knowledge-Based Platform for Transactional Electronic Services}, booktitle = {Lecture Notes in Computer Science}, year = {2002}, month = {9}, pages = {362-369}, publisher = {Springer Berlin Heidelberg}, organization = {Springer Berlin Heidelberg}, abstract = {Public transaction services (such as e-forms) although perceived the future of e-government have not yet realised their full potential. E-forms have a significant role in e-government, as they are the basis for implementing most of the twenty public services that all member states have to provide to their citizens and businesses. The aim of the SmartGov project is to specify, develop, deploy and evaluate a knowledge-based platform to assist public sector employees to generate online transaction services by simplifying their development, maintenance and integration with already installed IT systems. This platform will be evaluated in two European countries (in one Ministry and one Local Authority). This paper outlines key issues in the development of the SmartGov system platform.}, doi = {10.1007/978-3-540-46138-8_59}, url = {https://link.springer.com/chapter/10.1007/978-3-540-46138-8_59}, author = {P. Georgiadis and Giorgos Lepouras and Costas Vassilakis and G. Boukis and E. Tambouris and S. Gorilas and E. Davenport and A. Macintosh and J. Fraser and D. Lochhead} } @conference {599, title = {Agents in Decentralised Information Ecosystems: The DIET Approach}, booktitle = {Symposium on Information Agents for E-Commerce, AISB Convention}, year = {2001}, month = {March}, address = {University of York, United Kingdom}, abstract = {The complexity of the current global information infrastructure requires novel means of understanding and exploiting the dynamics of information. One means may be through the concept of an information ecosystem. An information ecosystem is analogous to a natural ecosystem in which there are flows of materials and energy analogous to information flow between many interacting individuals. This paper describes a multi-agent platform, DIET (Decentralised Information Ecosystem Technologies) that can be used to implement open, robust, adaptive and scalable ecosystem-inspired systems. We describe the design principles of the DIET software architecture, and present a simple example application based upon it. We go on to consider how the DIET system can be used to develop information brokering agents, and how these can contribute to the implementation of economic interactions between agents, as well as identifying some open questions relating to research in these areas. In this way we show the capacity of the DIET system to support applications using information agents.}, author = {P. Marrow and Manolis Koubarakis and R.H. van Lengen and F. Valverde-Albacete and E. Bonsma and J. Cid-Suerio and A.R. Figueiras-Vidal and A. Gallardo-Antolin and C. Hoile and T. Koutris and H. Molina-Bulla and A. Navia-Vazquez and Paraskevi Raftopoulou and N. Skarmeas and Christos Tryfonopoulos and F. Wang and C. Xiruhaki} } @conference {aisb01-MKL+, title = {Agents in Decentralised Information Ecosystems: The DIET Approach}, booktitle = {Proccedings of the Symposium on Information Agents for E-Commerce, AISB Convention}, year = {2001}, month = {March}, pages = {109{\textendash}117}, address = {York, UK}, author = {P. Marrow and Manolis Koubarakis and R.H. van Lengen and F. Valverde-Albacete and E. Bonsma and J. Cid-Suerio and A.R. Figueiras-Vidal and A. Gallardo-Antolin and C. Hoile and T. Koutris and H. Molina-Bulla and A. Navia-Vazquez and Paraskevi Raftopoulou and N. Skarmeas and Christos Tryfonopoulos and F. Wang and C. Xiruhaki} } @conference {1993-vassilakis-hci, title = {A Methodology for the Implementation of Software for the Design of Distributed Systems}, booktitle = {Proceedings of the 4th Hellenic Conference on Informatics}, year = {1993}, month = {12}, pages = {673{\textendash}686}, abstract = {As the use of distributed systems is spreading, and applications designed for such systems become more and more demanding, optimal design of distributed systems becomes a critical issue. Designing a distributed system has become more complex, due to the number of alternatives for each decision that must be made and because of the existence of many parameters which influence the overal performance of the distributed system. Thus, it is necessary to use software tools, capable of accepting a description of the user{\textquoteright}s requirements and suggesting solutions to the problem of designing a distributed system which meets the user{\textquoteright}s requirements. In this paper, we present a disciplined approach to the construction of such a software tool, which combines methods from the Artificial Intelligence domain, that are used in order to design the distributed system, along with simulation techniques, used to estimate the system{\textquoteright}s overall performance.}, author = {Costas Vassilakis and P. Georgiadis and D. Lelis and D. Mouzakis and M. Nikolaidou} }