104 research outputs found
Scan4Reco, Horizon 2020 Project
No full textOur cultural heritage and the way we preserve and valorise it is a major factor in defining Europe's place in the world and its attractiveness as a place to live, work, and visit; a powerful instrument that provides a sense of belonging amongst and between European citizens. The need to preserve, provide advanced access to and understanding of cultural heritage is clearly of utmost importance, especially when considering its wealth throughout Europe.
Scan4Reco project aims deliver a multi-modal and multi-discipline platform that will be able to non-destructively scan any cultural asset Scan4Reco introduces a novel and innovative approach for the scientific and wider community as it enables the processing of multi-sensorial input in such a way that produces a hierarchical, multi-layered and multidimensional complete model of the object of interest. In addition, Scan4Reco will combine the object of interest with inter-disciplinary knowledge derived from the web and specific relevant datasets while also provide it with an automatic inference of its forthcoming state/shape in the future
Activity related biometrics for person authentication
No full textOne of the major challenges in human-machine interaction has always been the development of such techniques that are able to provide accurate human recognition, so as to other either personalized services or to protect critical infrastructures from unauthorized access. To this direction, a series of well stated and efficient methods have been proposed mainly based on biometric characteristics of the user. Despite the significant progress that has been achieved recently, there are still many open issues in the area, concerning not only the performance of the systems but also the intrusiveness of the collecting methods.
The current thesis deals with the investigation of novel, activity-related biometric traits and their potential for multiple and unobtrusive authentication based on the spatiotemporal analysis of human activities. In particular, it starts with an extensive bibliography review regarding the most important works in the area of biometrics, exhibiting and justifying in parallel the transition that is performed from the classic biometrics to the new concept of behavioural biometrics.
Based on previous works related to the human physiology and human motion and motivated by the intuitive assumption that different body types and different characters would produce distinguishable, and thus, valuable for biometric verification, activity-related traits, a new type of biometrics, the so-called prehension biometrics (i.e. the combined movement of reaching, grasping activities), is introduced and thoroughly studied herein. The analysis is performed via the so-called Activity hyper-Surfaces that form a dynamic movement-related manifold for the extraction of a series of behavioural features.
Thereafter, the focus is laid on the extraction of continuous soft biometric features and their efficient combination with state-of-the-art biometric approaches towards increased authentication performance and enhanced security in template storage via Soft biometric Keys. In this context, a novel and generic probabilistic framework is proposed that produces an enhanced matching probability based on the modelling of the systematic error induced during the estimation of the aforementioned soft biometrics and the efficient clustering of the soft biometric feature space.
Next, an extensive experimental evaluation of the proposed methodologies follows that effectively illustrates the increased authentication potential of the prehension-related biometrics and the significant advances in the recognition performance by the probabilistic framework. In particular, the prehension biometrics related biometrics is applied on several databases of ~100 different subjects in total performing a great variety of movements.
The carried out experiments simulate both episodic and multiple authentication scenarios, while contextual parameters, (i.e. the ergonomic-based quality factors of the human body) are also taken into account. Furthermore, the probabilistic framework for augmenting biometric recognition via soft biometrics is applied on top of two state-of-art biometric systems, i.e. a gait recognition (> 100 subjects)- and a 3D face recognition-based one (~55 subjects), exhibiting significant advances to their performance.
The thesis is concluded with an in-depth discussion summarizing the major achievements of the current work, as well as some possible drawbacks and other open issues of the proposed approaches that could be addressed in future works.Open Acces
Using activity-related behavioural features towards more effective automatic stress detection
No full textThis paper introduces activity-related behavioural features that can be automatically extracted from a computer system, with the aim to increase the effectiveness of automatic stress detection. The proposed features are based on processing of appropriate video and accelerometer recordings taken from the monitored subjects. For the purposes of the present study, an experiment was conducted that utilized a stress-induction protocol based on the stroop colour word test. Video, accelerometer and biosignal (Electrocardiogram and Galvanic Skin Response) recordings were collected from nineteen participants. Then, an explorative study was conducted by following a methodology mainly based on spatiotemporal descriptors (Motion History Images) that are extracted from video sequences. A large set of activity-related behavioural features, potentially useful for automatic stress detection, were proposed and examined. Experimental evaluation showed that several of these behavioural features significantly correlate to self-reported stress. Moreover, it was found that the use of the proposed features can significantly enhance the performance of typical automatic stress detection systems, commonly based on biosignal processing
A Probabilistic Framework for the Reliability Assessment of Crowd Sourcing Urban Traffic Reports
No full textAbstractIncidents produce heavy congestion in large urban traffic networks and therefore real time information about them (e.g. location, timestamp, type) can be very useful for the drivers. An efficient way of gathering this type of information is through a crowd sourcing reporting system that multimodal travellers may utilise for providing information about various incidents they witness to other interconnected users in the same network. After the incoming traffic reports are evaluated, they can be shared to other travellers who are approaching the location of the reported incidents. Travelers can use the reported information for improving their mobility status. Collecting information using crowd sourcing techniques has implications and risks that need to be addressed. One of the most important challenges in this regard is the estimation of the reliability of the incoming information, usually related to individual user reputation. To this end, the exploitation of a reliability assessment system is of profound importance for assuring that only accurate information is shared between interconnected users. This paper introduces an innovative crowd sourcing information assessment mechanism for urban travellers. The purpose of the proposed probabilistic framework is to estimate if a user-generated report is true or false, given a set of static and dynamic parameters. The latter describe contextual conditions occurring at the time when an incident is reported. The proposed model takes into account the current location and speed of the reporting user due to their impact on the reliability of an incoming report. The proposed probabilistic model was evaluated in a simulation environment. Preliminary results show that, based on a set of rational assumptions, the estimated reliability decreases with the distance from the reported event and the speed of the reporting user. Based on the estimates that our model produces, a reliable true/false recommendation system can be devised for evaluating the user generated reports
An AI-Enabled Framework for Real-Time Generation of News Articles Based on Big EO Data for Disaster Reporting
No full textIn the field of journalism, the collection and processing of information from different heterogeneous sources are difficult and time-consuming processes. In the context of the theory of journalism 3.0, where multimedia data can be extracted from different sources on the web, the possibility of creating a tool for the exploitation of Earth observation (EO) data, especially images by professionals belonging to the field of journalism, is explored. With the production of massive volumes of EO image data, the problem of their exploitation and dissemination to the public, specifically, by professionals in the media industry, arises. In particular, the exploitation of satellite image data from existing tools is difficult for professionals who are not familiar with image processing. In this scope, this article presents a new innovative platform that automates some of the journalistic practices. This platform includes several mechanisms allowing users to early detect and receive information about breaking news in real-time, retrieve EO Sentinel-2 images upon request for a certain event, and automatically generate a personalized article according to the writing style of the author. Through this platform, the journalists or editors can also make any modifications to the generated article before publishing. This platform is an added-value tool not only for journalists and the media industry but also for freelancers and article writers who use information extracted from EO data in their articles
Behavioural Network Traffic Analytics for Securing 5G Networks
No full textThe analysis of the network traffic in 5G networks is of high significance to the network security administrator, since it could allow for the identification of different behavioural groups and the distinction of anomalous from normal activity. The problem is the multi-dimensional nature of the data, e.g. SMS, call, Internet, services etc. that makes it difficult to analyse. This is even more challenging in 5G networks, compared to previous generation networks, since one more dimension is added to the traffic, representing different network slices. In this respect, activity that is normal in one slice can be anomalous in another. This paper presents a graph-based method for network mining and visualization of user activities in a mobile network. The raw multi- dimensional network traffic data are used for the construction of multiple multi-dimensional graph- based features that capture specific behavioural aspects for each user. Within each feature, graph matching techniques are applied in order to identify groups of users with similar behaviour. The dissimilarity results for each feature are combined using a multi-objective visualization method. The outcome is a data visualization in which users with similar behaviour are depicted as points close to each other. The network analyst is able to select the desired trade-off among the multiple features, and visually detect groups of users with similar behaviours, as well as possible anomalous clusters or outliers. Experimental evaluation of the proposed approach in several application scenarios verify its efficiency.© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
MoVA: A Visual Analytics Tool Providing Insight in the Big Mobile Network Data
No full textPart 7: New Methods and Tools for Big DataInternational audienceMobile networks have numerous exploitable vulnerabilities that enable malicious individuals to launch Denial of Service (DoS) attacks and affect network security and performance. The efficient detection and attribution of these anomalies are of major importance to the mobile network operators, especially since there is a vast amount of information collected, which renders the problem as a Big Data problem. Previous approaches focus on either anomaly detection methods, or visualization methods separately. In addition, they utilize solely either the signaling or the Call Detail Record (CDR) activity in the network. This paper presents MoVA (Mobile network Visual Analytics), a visual analytics tool for the detection and attribution of anomalies in mobile cellular networks which combines anomaly detection and visualization, and is applied on both signaling and CDR activity in the network. In order to address the large volume of the data, the proposed application starts with an aggregated overview of the whole network and allows the operator to gradually focus on smaller sets of data, using different levels of abstraction. The proposed visualization methods are able to differentiate between different user behaviors, and enable the analyst to have an insight in the mobile network operation and easily spot the anomalous mobile devices. Hypothesis formulation and validation methods are also provided, in order to enable the analyst to formulate network security-related hypotheses, and validate or reject them based on the results of the analysis
A light-weighted ANN architecture for the classification of cyber-threats in modern communication networks
No full textIn modern communication networks, the integrity of the security is of great importance, since the existence of cyber attacks may lead to disastrous financial and social consequences. The anomaly detection constitutes an essential part of network security. This paper proposes a two-stage procedure to provide a solution regarding the anomaly detection and threat identification. The proposed method is suitable for modern communication networks and upcoming smart networks. The first stage of the method concerns the detection of abnormal incidents and the second stage involves the identification of the type of cyber threats, in case of an attack. The method based on the development of artificial neural network models and the UNSW-NB15 dataset is used to validate the proposed methodology. The experimental results confirm that the proposed method identifies all type of threats in comparison to the already known methods that identify only the threats that appear frequently
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