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Dr. Stefanos Vrochidis received the Diploma degree in Electrical Engineering from Aristotle University of Thessaloniki, Greece, the MSc degree in Radio Frequency Communication Systems from University of Southampton and the PhD degree in Electronic Engineering from Queen Mary University of London. Currently, he is a Senior Researcher (Grade C) with the Multimedia Knowledge and Social Media analytics Lab of ITI-CERTH and the Head of the Multimodal Data Fusion and Analytics (M4D) Group. His research interests include multimedia analysis and retrieval, multimodal fusion, computer vision, multimodal analytics based on artificial intelligence. Dr. Vrochidis has participated in more than 40 European and National research projects. He has edited 3 books and authored more than 220 related scientific journal, conference and book chapter publications.
A valuable aspect during crime scene investigation is the digital documentation of the scene. Traditional means of documentation include photography and in situ measurements from experts for further analysis. Although 3D reconstruction of pertinent scenes has already been explored as a complementary tool in investigation pipelines, such technology is considered unfamiliar and not yet widely adopted. This is explained by the expensive and specialised digitisation equipment that is available so far. However, the emergence of high-precision but low-cost devices capable of scanning scenes or objects in 3D has been proven as a reliable alternative to their counterparts. This paper summarises and analyses the state-of-the-art technologies in scene documentation using 3D digitisation and assesses the usefulness in typical police-related situations and the forensics domain in general. We present the methodology for acquiring data for 3D reconstruction of various types of scenes. Emphasis is placed on the applicability of each technique in a wide range of situations, ranging in type and size. The application of each reconstruction method is considered in this context and compared with respect to additional constraints, such as time availability and simplicity of operation of the corresponding scanning modality. To further support our findings, we release a multi-modal dataset obtained from a hypothetical indoor crime scene to the public.
George Galanakis; Xenophon Zabulis; Theodore Evdaimon; Sven-Eric Fikenscher; Sebastian Allertseder; Theodora Tsikrika; Stefanos Vrochidis. A Study of 3D Digitisation Modalities for Crime Scene Investigation. Forensic Sciences 2021, 1, 56 -85.
AMA StyleGeorge Galanakis, Xenophon Zabulis, Theodore Evdaimon, Sven-Eric Fikenscher, Sebastian Allertseder, Theodora Tsikrika, Stefanos Vrochidis. A Study of 3D Digitisation Modalities for Crime Scene Investigation. Forensic Sciences. 2021; 1 (2):56-85.
Chicago/Turabian StyleGeorge Galanakis; Xenophon Zabulis; Theodore Evdaimon; Sven-Eric Fikenscher; Sebastian Allertseder; Theodora Tsikrika; Stefanos Vrochidis. 2021. "A Study of 3D Digitisation Modalities for Crime Scene Investigation." Forensic Sciences 1, no. 2: 56-85.
Given the increasing occurrence of deviant activities in online platforms, it is of paramount importance to develop methods and tools that allow in-depth analysis and understanding to then develop effective countermeasures. This work proposes a framework towards detecting statistically significant change points in terrorism-related time series, which may indicate the occurrence of events to be paid attention to. These change points may reflect changes in the attitude towards and/or engagement with terrorism-related activities and events, possibly signifying, for instance, an escalation in the radicalization process. In particular, the proposed framework involves: (i) classification of online textual data as terrorism- and hate speech-related, which can be considered as indicators of a potential criminal or terrorist activity; and (ii) change point analysis in the time series generated by these data. The use of change point detection (CPD) algorithms in the produced time series of the aforementioned indicators—either in a univariate or two-dimensional case—can lead to the estimation of statistically significant changes in their structural behavior at certain time locations. To evaluate the proposed framework, we apply it on a publicly available dataset related to jihadist forums. Finally, topic detection on the estimated change points is implemented to further assess its effectiveness.
Ourania Theodosiadou; Kyriaki Pantelidou; Nikolaos Bastas; Despoina Chatzakou; Theodora Tsikrika; Stefanos Vrochidis; Ioannis Kompatsiaris. Change Point Detection in Terrorism-Related Online Content Using Deep Learning Derived Indicators. Information 2021, 12, 274 .
AMA StyleOurania Theodosiadou, Kyriaki Pantelidou, Nikolaos Bastas, Despoina Chatzakou, Theodora Tsikrika, Stefanos Vrochidis, Ioannis Kompatsiaris. Change Point Detection in Terrorism-Related Online Content Using Deep Learning Derived Indicators. Information. 2021; 12 (7):274.
Chicago/Turabian StyleOurania Theodosiadou; Kyriaki Pantelidou; Nikolaos Bastas; Despoina Chatzakou; Theodora Tsikrika; Stefanos Vrochidis; Ioannis Kompatsiaris. 2021. "Change Point Detection in Terrorism-Related Online Content Using Deep Learning Derived Indicators." Information 12, no. 7: 274.
Social media play an important role in the daily life of people around the globe and users have emerged as an active part of news distribution as well as production. The threatening pandemic of COVID-19 has been the lead subject in online discussions and posts, resulting to large amounts of related social media data, which can be utilised to reinforce the crisis management in several ways. Towards this direction, we propose a novel framework to collect, analyse, and visualise Twitter posts, which has been tailored to specifically monitor the virus spread in severely affected Italy. We present and evaluate a deep learning localisation technique that geotags posts based on the locations mentioned in their text, a face detection algorithm to estimate the number of people appearing in posted images, and a community detection approach to identify communities of Twitter users. Moreover, we propose further analysis of the collected posts to predict their reliability and to detect trending topics and events. Finally, we demonstrate an online platform that comprises an interactive map to display and filter analysed posts, utilising the outcome of the localisation technique, and a visual analytics dashboard that visualises the results of the topic, community, and event detection methodologies.
Stelios Andreadis; Gerasimos Antzoulatos; Thanassis Mavropoulos; Panagiotis Giannakeris; Grigoris Tzionis; Nick Pantelidis; Konstantinos Ioannidis; Anastasios Karakostas; Ilias Gialampoukidis; Stefanos Vrochidis; Ioannis Kompatsiaris. A social media analytics platform visualising the spread of COVID-19 in Italy via exploitation of automatically geotagged tweets. Online Social Networks and Media 2021, 23, 100134 .
AMA StyleStelios Andreadis, Gerasimos Antzoulatos, Thanassis Mavropoulos, Panagiotis Giannakeris, Grigoris Tzionis, Nick Pantelidis, Konstantinos Ioannidis, Anastasios Karakostas, Ilias Gialampoukidis, Stefanos Vrochidis, Ioannis Kompatsiaris. A social media analytics platform visualising the spread of COVID-19 in Italy via exploitation of automatically geotagged tweets. Online Social Networks and Media. 2021; 23 ():100134.
Chicago/Turabian StyleStelios Andreadis; Gerasimos Antzoulatos; Thanassis Mavropoulos; Panagiotis Giannakeris; Grigoris Tzionis; Nick Pantelidis; Konstantinos Ioannidis; Anastasios Karakostas; Ilias Gialampoukidis; Stefanos Vrochidis; Ioannis Kompatsiaris. 2021. "A social media analytics platform visualising the spread of COVID-19 in Italy via exploitation of automatically geotagged tweets." Online Social Networks and Media 23, no. : 100134.
The continuing advancements in technology have resulted in an explosion in the use of interconnected devices and sensors. Internet-of-Things (IoT) systems are used to provide remote solutions in different domains, like healthcare and security. A common service offered by IoT systems is the estimation of a person’s position in indoor spaces, which is quite often achieved with the exploitation of the Received Signal Strength Indication (RSSI). Localization tasks with the goal to locate the room are actually classification problems. Motivated by a current project, where there is the need to locate a missing child in crowded spaces, we intend to test the added value of using an accelerometer along with RSSI for room-level localization and assess the performance of ensemble learning methods. We present here the results of this preliminary approach of the early and late fusion of RSSI and accelerometer features in room-level localization. We further test the performance of the feature extraction from RSSI values. The classification algorithms and the fusion methods used to predict the room were evaluated using different protocols applied to a public dataset. The experimental results revealed better performance of the RSSI extracted features, while the accelerometer’s individual performance was poor and subsequently affected the fusion results.
Athina Tsanousa; Vasileios-Rafail Xefteris; Georgios Meditskos; Stefanos Vrochidis; Ioannis Kompatsiaris. Combining RSSI and Accelerometer Features for Room-Level Localization. Sensors 2021, 21, 2723 .
AMA StyleAthina Tsanousa, Vasileios-Rafail Xefteris, Georgios Meditskos, Stefanos Vrochidis, Ioannis Kompatsiaris. Combining RSSI and Accelerometer Features for Room-Level Localization. Sensors. 2021; 21 (8):2723.
Chicago/Turabian StyleAthina Tsanousa; Vasileios-Rafail Xefteris; Georgios Meditskos; Stefanos Vrochidis; Ioannis Kompatsiaris. 2021. "Combining RSSI and Accelerometer Features for Room-Level Localization." Sensors 21, no. 8: 2723.
Earth Observation (EO) Big Data Collections are acquired at large volumes and variety, due to their high heterogeneous nature. The multimodal character of EO Big Data requires effective combination of multiple modalities for similarity search. We propose a late fusion mechanism of multiple rankings to combine the results from several uni-modal searches in Sentinel 2 image collections. We fist create a K-order tensor from the results of separate searches by visual features, concepts, spatial and temporal information. Visual concepts and features are based on a vector representation from Deep Convolutional Neural Networks. 2D-surfaces of the K-order tensor initially provide candidate retrieved results per ranking position and are merged to obtain the final list of retrieved results. Satellite image patches are used as queries in order to retrieve the most relevant image patches in Sentinel 2 images. Quantitative and qualitative results show that the proposed method outperforms search by a single modality and other late fusion methods.
Ilias Gialampoukidis; Anastasia Moumtzidou; Marios Bakratsas; Stefanos Vrochidis; Ioannis Kompatsiaris. A Multimodal Tensor-Based Late Fusion Approach for Satellite Image Search in Sentinel 2 Images. Transactions on Petri Nets and Other Models of Concurrency XV 2021, 294 -306.
AMA StyleIlias Gialampoukidis, Anastasia Moumtzidou, Marios Bakratsas, Stefanos Vrochidis, Ioannis Kompatsiaris. A Multimodal Tensor-Based Late Fusion Approach for Satellite Image Search in Sentinel 2 Images. Transactions on Petri Nets and Other Models of Concurrency XV. 2021; ():294-306.
Chicago/Turabian StyleIlias Gialampoukidis; Anastasia Moumtzidou; Marios Bakratsas; Stefanos Vrochidis; Ioannis Kompatsiaris. 2021. "A Multimodal Tensor-Based Late Fusion Approach for Satellite Image Search in Sentinel 2 Images." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 294-306.
In what has arguably been one of the most troubling periods of recent medical history, with a global pandemic emphasising the importance of staying healthy, innovative tools that shelter patient well-being gain momentum. In that view, a framework is proposed that leverages multimodal data, namely inertial and depth sensor-originating data, can be integrated in health care-oriented platforms, and tackles the crucial task of human action recognition (HAR). To analyse person movement and consequently assess the patient’s condition, an effective methodology is presented that is two-fold: initially, Kinect-based action representations are constructed from handcrafted 3DHOG depth features and the descriptive power of a Fisher encoding scheme. This is complemented by wearable sensor data analysis, using time domain features and then boosted by exploring fusion strategies of minimum expense. Finally, an extended experimental process reveals competitive results in a well-known benchmark dataset and indicates the applicability of our methodology for HAR.
Panagiotis Giannakeris; Athina Tsanousa; Thanasis Mavropoulos; Georgios Meditskos; Konstantinos Ioannidis; Stefanos Vrochidis; Ioannis Kompatsiaris. Fusion of Multimodal Sensor Data for Effective Human Action Recognition in the Service of Medical Platforms. Transactions on Petri Nets and Other Models of Concurrency XV 2021, 12573, 367 -378.
AMA StylePanagiotis Giannakeris, Athina Tsanousa, Thanasis Mavropoulos, Georgios Meditskos, Konstantinos Ioannidis, Stefanos Vrochidis, Ioannis Kompatsiaris. Fusion of Multimodal Sensor Data for Effective Human Action Recognition in the Service of Medical Platforms. Transactions on Petri Nets and Other Models of Concurrency XV. 2021; 12573 ():367-378.
Chicago/Turabian StylePanagiotis Giannakeris; Athina Tsanousa; Thanasis Mavropoulos; Georgios Meditskos; Konstantinos Ioannidis; Stefanos Vrochidis; Ioannis Kompatsiaris. 2021. "Fusion of Multimodal Sensor Data for Effective Human Action Recognition in the Service of Medical Platforms." Transactions on Petri Nets and Other Models of Concurrency XV 12573, no. : 367-378.
During the last decades, massive amounts of satellite images are becoming available that can be enriched with semantic annotations for the creation of value-added Earth Observation products. One challenge is to extract knowledge from the raw satellite data in an automated way and to effectively manage the extracted information in a semantic way, to allow fast and accurate decisions of spatio-temporal nature in a real operational scenario. In this work we present a framework that combines supervised learning for crop type classification on satellite imagery time-series with Semantic Web and Linked Data technologies to assist in the implementation of rule sets by the European Common Agricultural Policy (CAP). The framework collects georeferenced data that are available online and satellite images from the Sentinel-2 mission. We analyze image time-series that cover the entire cultivation period and link each parcel with a specific crop. On top of that, we introduce a semantic layer to facilitate a knowledge-driven management of the available information, capitalizing on ontologies for knowledge representation and semantic rules, to identify possible farmers non-compliance according to the Greening 1 (Crop Diversification) and SMR 1 rule (protection of waters against pollution caused by nitrates) rules of the CAP. Experiments show the effectiveness of the proposed integrated approach in three different scenarios for crop type monitoring and consistency-checking for non-compliance to the CAP rules; the smart sampling of on-the-spot-checks; the automatic detection of CAP's Greening 1 rule; and the automatic detection of susceptible parcels according to the CAP's SMR 1 rule.
Maria Rousi; Vasileios Sitokonstantinou; Georgios Meditskos; Ioannis Papoutsis; Ilias Gialampoukidis; Alkiviadis Koukos; Vassilia Karathanassi; Thanassis Drivas; Stefanos Vrochidis; Charalampos Kontoes; Ioannis Kompatsiaris. Semantically Enriched Crop Type Classification and Linked Earth Observation Data to Support the Common Agricultural Policy Monitoring. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 14, 529 -552.
AMA StyleMaria Rousi, Vasileios Sitokonstantinou, Georgios Meditskos, Ioannis Papoutsis, Ilias Gialampoukidis, Alkiviadis Koukos, Vassilia Karathanassi, Thanassis Drivas, Stefanos Vrochidis, Charalampos Kontoes, Ioannis Kompatsiaris. Semantically Enriched Crop Type Classification and Linked Earth Observation Data to Support the Common Agricultural Policy Monitoring. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 14 (99):529-552.
Chicago/Turabian StyleMaria Rousi; Vasileios Sitokonstantinou; Georgios Meditskos; Ioannis Papoutsis; Ilias Gialampoukidis; Alkiviadis Koukos; Vassilia Karathanassi; Thanassis Drivas; Stefanos Vrochidis; Charalampos Kontoes; Ioannis Kompatsiaris. 2020. "Semantically Enriched Crop Type Classification and Linked Earth Observation Data to Support the Common Agricultural Policy Monitoring." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14, no. 99: 529-552.
Although Augmented Reality (AR) technology has entered many market and knowledge domains such as games and leisure activities, it remains rather limited in digital heritage. After studying the potentiality of using modern AR elements in a museum context, this paper proposes the use of additional game and educational elements in the core AR application in order to enhance the overall on-the-spot museum visitor’s experience. An agile AR application design methodology was followed by taking into account the needs of small-to-medium sized real-world museums. Moreover, a heuristic evaluation protocol was applied by a group of experts in order to test the proof-of-concept AR application, in which some novel elements were proposed such as the AR quiz game. The main findings indicate that enhanced AR experiences in museum settings can make a nice fit with the user environment, physical and perceptual abilities, known metaphors, and user position and motion in 3D space. Moreover, AR services can be provided under a minimum distraction and physical effort. As a conclusion, AR technologies are mature enough to be standardized for museum usage, while the audience seems to be ready to take advantage of the related enhanced museum experiences to maximize both user satisfaction and learning outcomes.
Ioannis Paliokas; Athanasios Patenidis; Eirini Mitsopoulou; Christina Tsita; George Pehlivanides; Elli Karyati; Spyros Tsafaras; Evangelos Stathopoulos; Alexandros Kokkalas; Sotiris Diplaris; Georgios Meditskos; Stefanos Vrochidis; Eleana Tasiopoulou; Christodoulos Riggas; Konstantinos Votis; Ioannis Kompatsiaris; Dimitrios Tzovaras. A Gamified Augmented Reality Application for Digital Heritage and Tourism. Applied Sciences 2020, 10, 7868 .
AMA StyleIoannis Paliokas, Athanasios Patenidis, Eirini Mitsopoulou, Christina Tsita, George Pehlivanides, Elli Karyati, Spyros Tsafaras, Evangelos Stathopoulos, Alexandros Kokkalas, Sotiris Diplaris, Georgios Meditskos, Stefanos Vrochidis, Eleana Tasiopoulou, Christodoulos Riggas, Konstantinos Votis, Ioannis Kompatsiaris, Dimitrios Tzovaras. A Gamified Augmented Reality Application for Digital Heritage and Tourism. Applied Sciences. 2020; 10 (21):7868.
Chicago/Turabian StyleIoannis Paliokas; Athanasios Patenidis; Eirini Mitsopoulou; Christina Tsita; George Pehlivanides; Elli Karyati; Spyros Tsafaras; Evangelos Stathopoulos; Alexandros Kokkalas; Sotiris Diplaris; Georgios Meditskos; Stefanos Vrochidis; Eleana Tasiopoulou; Christodoulos Riggas; Konstantinos Votis; Ioannis Kompatsiaris; Dimitrios Tzovaras. 2020. "A Gamified Augmented Reality Application for Digital Heritage and Tourism." Applied Sciences 10, no. 21: 7868.
Recent developments in remote sensing have shown that snow depth can be estimated accurately on a global scale using satellite images through cross-polarization and copolarization backscatter measurements. This method does, however, have some limitations in low-land areas with dense forest coverage and shallow snow, which are often found nearby urban areas. In these areas, citizen observations can be fused with satellite-based estimations to deliver more accurate solutions. To that end, we use snow-related tweets that have been annotated by artificial intelligence (AI) methods and are introduced in a novel neural network model, aiming to increase the estimation accuracy of the state-of-the-art remote sensing method. The proposed model combines the estimated snow depth from Sentinel 1 images with the number of Twitter posts and Twitter images that are semantically relevant to snow. The use of instant social media data for purposes of snow depth estimation is investigated, validated, and tested in Finland. Our results show that this approach does improve the snow depth estimation, highlighting its potential for use in civil protection agencies in managing snow conditions.
Damianos Florin Mantsis; Marios Bakratsas; Stelios Andreadis; Petteri Karsisto; Anastasia Moumtzidou; Ilias Gialampoukidis; Ari Karppinen; Stefanos Vrochidis; Ioannis Kompatsiaris. Multimodal Fusion of Sentinel 1 Images and Social Media Data for Snow Depth Estimation. IEEE Geoscience and Remote Sensing Letters 2020, PP, 1 -5.
AMA StyleDamianos Florin Mantsis, Marios Bakratsas, Stelios Andreadis, Petteri Karsisto, Anastasia Moumtzidou, Ilias Gialampoukidis, Ari Karppinen, Stefanos Vrochidis, Ioannis Kompatsiaris. Multimodal Fusion of Sentinel 1 Images and Social Media Data for Snow Depth Estimation. IEEE Geoscience and Remote Sensing Letters. 2020; PP (99):1-5.
Chicago/Turabian StyleDamianos Florin Mantsis; Marios Bakratsas; Stelios Andreadis; Petteri Karsisto; Anastasia Moumtzidou; Ilias Gialampoukidis; Ari Karppinen; Stefanos Vrochidis; Ioannis Kompatsiaris. 2020. "Multimodal Fusion of Sentinel 1 Images and Social Media Data for Snow Depth Estimation." IEEE Geoscience and Remote Sensing Letters PP, no. 99: 1-5.
A novel first-person human activity recognition framework is proposed in this work. Our proposed methodology is inspired by the central role moving objects have in egocentric activity videos. Using a Deep Convolutional Neural Network we detect objects and develop discriminant object flow histograms in order to represent fine-grained micro-actions during short temporal windows. Our framework is based on the assumption that large scale activities are synthesized by fine-grained micro-actions. We gather all the micro-actions and perform Gaussian Mixture Model clusterization, so as to build a micro-action vocabulary that is later used in a Fisher encoding schema. Results show that our method can reach 60% recognition rate on the benchmark ADL dataset. The capabilities of the proposed framework are also showcased by profoundly evaluating for a great deal of hyper-parameters and comparing to other State-of-the-Art works.
Panagiotis Giannakeris; Panagiotis C. Petrantonakis; Konstantinos Avgerinakis; Stefanos Vrochidis; Ioannis Kompatsiaris. First-person activity recognition from micro-action representations using convolutional neural networks and object flow histograms. Multimedia Tools and Applications 2020, 80, 22487 -22507.
AMA StylePanagiotis Giannakeris, Panagiotis C. Petrantonakis, Konstantinos Avgerinakis, Stefanos Vrochidis, Ioannis Kompatsiaris. First-person activity recognition from micro-action representations using convolutional neural networks and object flow histograms. Multimedia Tools and Applications. 2020; 80 (15):22487-22507.
Chicago/Turabian StylePanagiotis Giannakeris; Panagiotis C. Petrantonakis; Konstantinos Avgerinakis; Stefanos Vrochidis; Ioannis Kompatsiaris. 2020. "First-person activity recognition from micro-action representations using convolutional neural networks and object flow histograms." Multimedia Tools and Applications 80, no. 15: 22487-22507.
Despite the fact that automatic content analysis has made remarkable progress over the last decade - mainly due to significant advances in machine learning - interactive video retrieval is still a very challenging problem, with an increasing relevance in practical applications. The Video Browser Showdown (VBS) is an annual evaluation competition that pushes the limits of interactive video retrieval with state-of-the-art tools, tasks, data, and evaluation metrics. In this paper, we analyse the results and outcome of the 8th iteration of the VBS in detail. We first give an overview of the novel and considerably larger V3C1 dataset and the tasks that were performed during VBS 2019. We then go on to describe the search systems of the six international teams in terms of features and performance. And finally, we perform an in-depth analysis of the per-team success ratio and relate this to the search strategies that were applied, the most popular features, and problems that were experienced. A large part of this analysis was conducted based on logs that were collected during the competition itself. This analysis gives further insights into the typical search behavior and differences between expert and novice users. Our evaluation shows that textual search and content browsing are the most important aspects in terms of logged user interactions. Furthermore, we observe a trend towards deep learning based features, especially in the form of labels generated by artificial neural networks. But nevertheless, for some tasks, very specific content-based search features are still being used. We expect these findings to contribute to future improvements of interactive video search systems.
Luca Rossetto; Ralph Gasser; Jakub Lokoc; Werner Bailer; Klaus Schoeffmann; Bernd Muenzer; Tomas Soucek; Phuong Anh Nguyen; Paolo Bolettieri; Andreas Leibetseder; Stefanos Vrochidis. Interactive Video Retrieval in the Age of Deep Learning – Detailed Evaluation of VBS 2019. IEEE Transactions on Multimedia 2020, 23, 243 -256.
AMA StyleLuca Rossetto, Ralph Gasser, Jakub Lokoc, Werner Bailer, Klaus Schoeffmann, Bernd Muenzer, Tomas Soucek, Phuong Anh Nguyen, Paolo Bolettieri, Andreas Leibetseder, Stefanos Vrochidis. Interactive Video Retrieval in the Age of Deep Learning – Detailed Evaluation of VBS 2019. IEEE Transactions on Multimedia. 2020; 23 (99):243-256.
Chicago/Turabian StyleLuca Rossetto; Ralph Gasser; Jakub Lokoc; Werner Bailer; Klaus Schoeffmann; Bernd Muenzer; Tomas Soucek; Phuong Anh Nguyen; Paolo Bolettieri; Andreas Leibetseder; Stefanos Vrochidis. 2020. "Interactive Video Retrieval in the Age of Deep Learning – Detailed Evaluation of VBS 2019." IEEE Transactions on Multimedia 23, no. 99: 243-256.
In human activity recognition studies it is important to identify an optimal set with the minimum number of features that will potentially improve the recognition rate. In the current paper we introduce a promising feature selection method that exploits the differences on the correlation structure of the features, between the different classes of the target variable. Using the recordings of triaxial accelerometers and gyroscopes, we extracted several features and created subsets according to the activities performed. For each subset, we calculated the pairwise correlation coefficients of the features and compared the feature correlations of different subsets. By identifying the significantly different correlations we ranked the variables participating in those correlations based on their frequency of appearance and thus created a subset of features that will optimize the performance of a classification algorithm. The method allows the researcher to select the desired number of features to be included in the classification. Two publicly available datasets were used to evaluate the performance of the proposed methodology in binary and multiclass classification problems. The evaluation revealed quite promising results of the methodology that was compared to the performance of the whole feature set and of a feature selection method that has been extensively used in activity recognition studies.
Athina Tsanousa; Georgios Meditskos; Stefanos Vrochidis; Lefteris Angelis. A novel feature selection method based on comparison of correlations for human activity recognition problems. Journal of Ambient Intelligence and Humanized Computing 2020, 11, 5961 -5975.
AMA StyleAthina Tsanousa, Georgios Meditskos, Stefanos Vrochidis, Lefteris Angelis. A novel feature selection method based on comparison of correlations for human activity recognition problems. Journal of Ambient Intelligence and Humanized Computing. 2020; 11 (12):5961-5975.
Chicago/Turabian StyleAthina Tsanousa; Georgios Meditskos; Stefanos Vrochidis; Lefteris Angelis. 2020. "A novel feature selection method based on comparison of correlations for human activity recognition problems." Journal of Ambient Intelligence and Humanized Computing 11, no. 12: 5961-5975.
Conversational agents are reshaping our communication environment and have the potential to inform and persuade in new and effective ways. In this paper, we present the underlying technologies and the theoretical background behind a health-care platform dedicated to supporting medical stuff and individuals with movement disabilities and to providing advanced monitoring functionalities in hospital and home surroundings. The framework implements an intelligent combination of two research areas: (1) sensor- and camera-based monitoring to collect, analyse, and interpret people behaviour and (2) natural machine–human interaction through an apprehensive virtual assistant benefiting ailing patients. In addition, the framework serves as an important assistant to caregivers and clinical experts to obtain information about the patients in an intuitive manner. The proposed approach capitalises on latest breakthroughs in computer vision, sensor management, speech recognition, natural language processing, knowledge representation, dialogue management, semantic reasoning, and speech synthesis, combining medical expertise and patient history.
Thanassis Mavropoulos; Georgios Meditskos; Spyridon Symeonidis; Eleni Kamateri; Maria Rousi; Dimitris Tzimikas; Lefteris Papageorgiou; Christos Eleftheriadis; George Adamopoulos; Stefanos Vrochidis; Ioannis Kompatsiaris. A Context-Aware Conversational Agent in the Rehabilitation Domain. Future Internet 2019, 11, 231 .
AMA StyleThanassis Mavropoulos, Georgios Meditskos, Spyridon Symeonidis, Eleni Kamateri, Maria Rousi, Dimitris Tzimikas, Lefteris Papageorgiou, Christos Eleftheriadis, George Adamopoulos, Stefanos Vrochidis, Ioannis Kompatsiaris. A Context-Aware Conversational Agent in the Rehabilitation Domain. Future Internet. 2019; 11 (11):231.
Chicago/Turabian StyleThanassis Mavropoulos; Georgios Meditskos; Spyridon Symeonidis; Eleni Kamateri; Maria Rousi; Dimitris Tzimikas; Lefteris Papageorgiou; Christos Eleftheriadis; George Adamopoulos; Stefanos Vrochidis; Ioannis Kompatsiaris. 2019. "A Context-Aware Conversational Agent in the Rehabilitation Domain." Future Internet 11, no. 11: 231.
We present the technologies and the theoretical background of an intelligent interconnected infrastructure for public security and safety. The innovation of the framework lies in the intelligent combination of devices and human information towards human and situational awareness, so as to provide a protection and security environment for citizens. The framework is currently being used to support visitors in public spaces and events, by creating the appropriate infrastructure to address a set of urgent situations, such as health-related problems and missing children in overcrowded environments, supporting smart links between humans and entities on the basis of goals, and adapting device operation to comply with human objectives, profiles, and privacy. State-of-the-art technologies in the domain of IoT data collection and analytics are combined with localization techniques, ontologies, reasoning mechanisms, and data aggregation in order to acquire a better understanding of the ongoing situation and inform the necessary people and devices to act accordingly. Finally, we present the first results of people localization and the platforms’ ontology and representation framework.
Angelos Chatzimichail; Christos Chatzigeorgiou; Athina Tsanousa; Dimos Ntioudis; Georgios Meditskos; Fotis Andritsopoulos; Christina Karaberi; Panagiotis Kasnesis; Dimitrios G. Kogias; Georgios Gorgogetas; Stefanos Vrochidis; Charalampos Patrikakis; Ioannis Kompatsiaris. Internet of Things Infrastructure for Security and Safety in Public Places. Information 2019, 10, 333 .
AMA StyleAngelos Chatzimichail, Christos Chatzigeorgiou, Athina Tsanousa, Dimos Ntioudis, Georgios Meditskos, Fotis Andritsopoulos, Christina Karaberi, Panagiotis Kasnesis, Dimitrios G. Kogias, Georgios Gorgogetas, Stefanos Vrochidis, Charalampos Patrikakis, Ioannis Kompatsiaris. Internet of Things Infrastructure for Security and Safety in Public Places. Information. 2019; 10 (11):333.
Chicago/Turabian StyleAngelos Chatzimichail; Christos Chatzigeorgiou; Athina Tsanousa; Dimos Ntioudis; Georgios Meditskos; Fotis Andritsopoulos; Christina Karaberi; Panagiotis Kasnesis; Dimitrios G. Kogias; Georgios Gorgogetas; Stefanos Vrochidis; Charalampos Patrikakis; Ioannis Kompatsiaris. 2019. "Internet of Things Infrastructure for Security and Safety in Public Places." Information 10, no. 11: 333.
Oil spill is considered one of the main threats to marine and coastal environments. Efficient monitoring and early identification of oil slicks are vital for the corresponding authorities to react expediently, confine the environmental pollution and avoid further damage. Synthetic aperture radar (SAR) sensors are commonly used for this objective due to their capability for operating efficiently regardless of the weather and illumination conditions. Black spots probably related to oil spills can be clearly captured by SAR sensors, yet their discrimination from look-alikes poses a challenging objective. A variety of different methods have been proposed to automatically detect and classify these dark spots. Most of them employ custom-made datasets posing results as non-comparable. Moreover, in most cases, a single label is assigned to the entire SAR image resulting in a difficulties when manipulating complex scenarios or extracting further information from the depicted content. To overcome these limitations, semantic segmentation with deep convolutional neural networks (DCNNs) is proposed as an efficient approach. Moreover, a publicly available SAR image dataset is introduced, aiming to consist a benchmark for future oil spill detection methods. The presented dataset is employed to review the performance of well-known DCNN segmentation models in the specific task. DeepLabv3+ presented the best performance, in terms of test set accuracy and related inference time. Furthermore, the complex nature of the specific problem, especially due to the challenging task of discriminating oil spills and look-alikes is discussed and illustrated, utilizing the introduced dataset. Results imply that DCNN segmentation models, trained and evaluated on the provided dataset, can be utilized to implement efficient oil spill detectors. Current work is expected to contribute significantly to the future research activity regarding oil spill identification and SAR image processing.
Marios Krestenitis; Georgios Orfanidis; Konstantinos Ioannidis; Konstantinos Avgerinakis; Stefanos Vrochidis; Ioannis Kompatsiaris. Oil Spill Identification from Satellite Images Using Deep Neural Networks. Remote Sensing 2019, 11, 1762 .
AMA StyleMarios Krestenitis, Georgios Orfanidis, Konstantinos Ioannidis, Konstantinos Avgerinakis, Stefanos Vrochidis, Ioannis Kompatsiaris. Oil Spill Identification from Satellite Images Using Deep Neural Networks. Remote Sensing. 2019; 11 (15):1762.
Chicago/Turabian StyleMarios Krestenitis; Georgios Orfanidis; Konstantinos Ioannidis; Konstantinos Avgerinakis; Stefanos Vrochidis; Ioannis Kompatsiaris. 2019. "Oil Spill Identification from Satellite Images Using Deep Neural Networks." Remote Sensing 11, no. 15: 1762.
Dialogue‐based systems often consist of several components, such as communication analysis, dialogue management, domain reasoning, and language generation. In this paper, we present Converness, an ontology‐driven, rule‐based framework to facilitate domain reasoning for conversational awareness in multimodal dialogue‐based agents. Converness uses Web Ontology Language 2 (OWL 2) ontologies to capture and combine the conversational modalities of the domain, for example, deictic gestures and spoken utterances, fuelling conversational topic understanding, and interpretation using description logics and rules. At the same time, defeasible rules are used to couple domain and user‐centred knowledge to further assist the interaction with end users, facilitating advanced conflict resolution and personalised context disambiguation. We illustrate the capabilities of the framework through its integration into a multimodal dialogue‐based agent that serves as an intelligent interface between users (elderly, caregivers, and health experts) and an ambient assistive living platform in real home settings.
Georgios Meditskos; Efstratios Kontopoulos; Stefanos Vrochidis; Ioannis Kompatsiaris. Converness: Ontology‐driven conversational awareness and context understanding in multimodal dialogue systems. Expert Systems 2019, 37, 1 .
AMA StyleGeorgios Meditskos, Efstratios Kontopoulos, Stefanos Vrochidis, Ioannis Kompatsiaris. Converness: Ontology‐driven conversational awareness and context understanding in multimodal dialogue systems. Expert Systems. 2019; 37 (1):1.
Chicago/Turabian StyleGeorgios Meditskos; Efstratios Kontopoulos; Stefanos Vrochidis; Ioannis Kompatsiaris. 2019. "Converness: Ontology‐driven conversational awareness and context understanding in multimodal dialogue systems." Expert Systems 37, no. 1: 1.
Identifying terrorism-related key actors in social media is of vital significance for law enforcement agencies and social media organizations in their effort to counter terrorism-related online activities. This work proposes a novel framework for the identification of key actors in multidimensional social networks formed by considering several different types of user relationships/interactions in social media. The framework is based on a mechanism which maps the multidimensional network to a single-layer network, where several centrality measures can then be employed for detecting the key actors. The effectiveness of the proposed framework for each centrality measure is evaluated by using well-established precision-oriented evaluation metrics against a ground truth dataset, and the experimental results indicate the promising performance of our key actor identification framework.
George Kalpakis; Theodora Tsikrika; Stefanos Vrochidis; Ioannis Kompatsiaris. Identifying Terrorism-Related Key Actors in Multidimensional Social Networks. Privacy Enhancing Technologies 2018, 93 -105.
AMA StyleGeorge Kalpakis, Theodora Tsikrika, Stefanos Vrochidis, Ioannis Kompatsiaris. Identifying Terrorism-Related Key Actors in Multidimensional Social Networks. Privacy Enhancing Technologies. 2018; ():93-105.
Chicago/Turabian StyleGeorge Kalpakis; Theodora Tsikrika; Stefanos Vrochidis; Ioannis Kompatsiaris. 2018. "Identifying Terrorism-Related Key Actors in Multidimensional Social Networks." Privacy Enhancing Technologies , no. : 93-105.
Stelios Andreadis; Anastasia Moumtzidou; Damianos Galanopoulos; Foteini Markatopoulou; Konstantinos Apostolidis; Thanassis Mavropoulos; Ilias Gialampoukidis; Stefanos Vrochidis; Vasileios Mezaris; Ioannis Kompatsiaris; Ioannis Patras. VERGE in VBS 2019. Privacy Enhancing Technologies 2018, 602 -608.
AMA StyleStelios Andreadis, Anastasia Moumtzidou, Damianos Galanopoulos, Foteini Markatopoulou, Konstantinos Apostolidis, Thanassis Mavropoulos, Ilias Gialampoukidis, Stefanos Vrochidis, Vasileios Mezaris, Ioannis Kompatsiaris, Ioannis Patras. VERGE in VBS 2019. Privacy Enhancing Technologies. 2018; ():602-608.
Chicago/Turabian StyleStelios Andreadis; Anastasia Moumtzidou; Damianos Galanopoulos; Foteini Markatopoulou; Konstantinos Apostolidis; Thanassis Mavropoulos; Ilias Gialampoukidis; Stefanos Vrochidis; Vasileios Mezaris; Ioannis Kompatsiaris; Ioannis Patras. 2018. "VERGE in VBS 2019." Privacy Enhancing Technologies , no. : 602-608.
Analysts and journalists face the problem of having to deal with very large, heterogeneous, and multilingual data volumes that need to be analyzed, understood, and aggregated. Automated and simplified editorial and authoring process could significantly reduce time, labor, and costs. Therefore, there is a need for unified access to multilingual and multicultural news story material, beyond the level of a nation, ensuring context-aware, spatiotemporal, and semantic interpretation, correlating also and summarizing the interpreted material into a coherent gist. In this paper, we present a platform integrating multimodal analytics techniques, which are able to support journalists in handling large streams of real-time and diverse information. Specifically, the platform automatically crawls and indexes multilingual and multimedia information from heterogeneous resources. Textual information is automatically summarized and can be translated (on demand) into the language of the journalist. High-level information is extracted from both textual and multimedia content for fast inspection using concept clouds. The textual and multimedia content is semantically integrated and indexed using a common representation, to be accessible through a web-based search engine. The evaluation of the proposed platform was performed by several groups of journalists revealing satisfaction from the user side.
Stefanos Vrochidis; Anastasia Moumtzidou; Ilias Gialampoukidis; Dimitris Liparas; Gerard Casamayor; Leo Wanner; Nicolaus Heise; Tilman Wagner; Andriy Bilous; Emmanuel Jamin; Boyan Simeonov; Vladimir Alexiev; Reinhard Busch; Ioannis Arapakis; Ioannis Kompatsiaris. A Multimodal Analytics Platform for Journalists Analyzing Large-Scale, Heterogeneous Multilingual, and Multimedia Content. Frontiers in Robotics and AI 2018, 5, 1 .
AMA StyleStefanos Vrochidis, Anastasia Moumtzidou, Ilias Gialampoukidis, Dimitris Liparas, Gerard Casamayor, Leo Wanner, Nicolaus Heise, Tilman Wagner, Andriy Bilous, Emmanuel Jamin, Boyan Simeonov, Vladimir Alexiev, Reinhard Busch, Ioannis Arapakis, Ioannis Kompatsiaris. A Multimodal Analytics Platform for Journalists Analyzing Large-Scale, Heterogeneous Multilingual, and Multimedia Content. Frontiers in Robotics and AI. 2018; 5 ():1.
Chicago/Turabian StyleStefanos Vrochidis; Anastasia Moumtzidou; Ilias Gialampoukidis; Dimitris Liparas; Gerard Casamayor; Leo Wanner; Nicolaus Heise; Tilman Wagner; Andriy Bilous; Emmanuel Jamin; Boyan Simeonov; Vladimir Alexiev; Reinhard Busch; Ioannis Arapakis; Ioannis Kompatsiaris. 2018. "A Multimodal Analytics Platform for Journalists Analyzing Large-Scale, Heterogeneous Multilingual, and Multimedia Content." Frontiers in Robotics and AI 5, no. : 1.
The automatic extraction of relations between medical entities found in related texts is considered to be a very important task, due to the multitude of applications that it can support, from question answering systems to the development of medical ontologies. Many different methodologies have been presented and applied to this task over the years. Of particular interest are hybrid approaches, in which different techniques are combined in order to improve the individual performance of either one of them. In this study, we extend a previously established hybrid framework for medical relation extraction, which we modify by enhancing the pattern-based part of the framework and by applying a more sophisticated weighting method. Most notably, we replace the use of regular expressions with finite state automata for the pattern-building part, while the fusion part is replaced by a weighting strategy that is based on the operational capabilities of the Random Forests algorithm. The experimental results indicate the superiority of the proposed approach against the aforementioned well-established hybrid methodology and other state-of-the-art approaches.
Thanassis Mavropoulos; Dimitris Liparas; Spyridon Symeonidis; Stefanos Vrochidis; Ioannis Kompatsiaris. A Hybrid Approach for Biomedical Relation Extraction Using Finite State Automata and Random Forest-Weighted Fusion. Transactions on Petri Nets and Other Models of Concurrency XV 2018, 450 -462.
AMA StyleThanassis Mavropoulos, Dimitris Liparas, Spyridon Symeonidis, Stefanos Vrochidis, Ioannis Kompatsiaris. A Hybrid Approach for Biomedical Relation Extraction Using Finite State Automata and Random Forest-Weighted Fusion. Transactions on Petri Nets and Other Models of Concurrency XV. 2018; ():450-462.
Chicago/Turabian StyleThanassis Mavropoulos; Dimitris Liparas; Spyridon Symeonidis; Stefanos Vrochidis; Ioannis Kompatsiaris. 2018. "A Hybrid Approach for Biomedical Relation Extraction Using Finite State Automata and Random Forest-Weighted Fusion." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 450-462.