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Grammenos D.,Institute of Computer Science FORTH
Interactions | Year: 2016

Future Designers aims to be an experience that broadens one's thinking-not a lesson. The presented material is not meant to be learned or remembered. Like the fifth use of the designer's pillow, it is intended as a step to move further ahead. Probably the best short description of the course comes from one of the participating teachers, who exclaimed that "it feels like a rollercoaster for the mind!" The underlying philosophy behind Future Designers can be summed up this way: If at some point in your life you realize that you cannot change the world, the next best thing that you can do is to try to change those who one day may change it-the Future Designers. © 2016 ACM.


Padeleris P.,Institute of Computer Science FORTH | Zabulis X.,Institute of Computer Science FORTH | Argyros A.A.,Institute of Computer Science FORTH
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops | Year: 2012

We propose a method for human head pose estimation based on images acquired by a depth camera. During an initialization phase, a reference depth image of a human subject is obtained. At run time, the method searches the 6-dimensional pose space to find a pose from which the head appears identical to the reference view. This search is formulated as an optimization problem whose objective function quantifies the discrepancy of the depth measurements between the hypothesized views to the reference view. The method is demonstrated in several data sets including ones with known ground truth and comparatively evaluated with respect to state of the art methods. The obtained experimental results show that the proposed method outperforms existing methods in accuracy and tolerance to occlusions. Additionally, compared to the state of the art, it handles head pose estimation in a wider range of head poses. © 2012 IEEE.


Bikakis A.,Institute of Computer Science FORTH | Antoniou G.,Institute of Computer Science FORTH
IEEE Transactions on Knowledge and Data Engineering | Year: 2010

The imperfect nature of context in Ambient Intelligence environments and the special characteristics of the entities that possess and share the available context information render contextual reasoning a very challenging task. The accomplishment of this task requires formal models that handle the involved entities as autonomous logic-based agents and provide methods for handling the imperfect and distributed nature of context. This paper proposes a solution based on the Multi-Context Systems paradigm in which local context knowledge of ambient agents is encoded in rule theories (contexts), and information flow between agents is achieved through mapping rules that associate concepts used by different contexts. To handle imperfect context, we extend Multi-Context Systems with nonmonotonic features: local defeasible theories, defeasible mapping rules, and a preference ordering on the system contexts. On top of this model, we have developed an argumentation framework that exploits context and preference information to resolve potential conflicts caused by the interaction of ambient agents through the mappings, and a distributed algorithm for query evaluation. © 2010 IEEE.


Grammenos D.,Institute of Computer Science FORTH | Michel D.,Institute of Computer Science FORTH | Zabulis X.,Institute of Computer Science FORTH | Argyros A.A.,Institute of Computer Science FORTH | Argyros A.A.,University of Crete
Proceedings of the 5th International Conference on Tangible Embedded and Embodied Interaction, TEI'11 | Year: 2011

A frequent need of museums is to provide visitors with context-sensitive information about exhibits in the form of maps, or scale models. This paper suggests an augmented-reality approach for supplementing physical surfaces with digital information, through the use of pieces of plain paper that act as personal, location-aware, interactive screens. The technologies employed are presented, along with the interactive behavior of the system, which was instantiated and tested in the form of two prototype setups: a wooden table covered with a printed map and a glass case containing a scale model. The paper also discusses key issues stemming from experience and observations in the course of qualitative evaluation sessions. © 2011 ACM.


Saveta T.,Institute of Computer Science FORTH | Daskalaki E.,Institute of Computer Science FORTH | Flouris G.,Institute of Computer Science FORTH | Fundulaki I.,Institute of Computer Science FORTH | Ngonga Ngomo A.-C.,University of Leipzig
CEUR Workshop Proceedings | Year: 2016

Identifying duplicate instances in the Data Web is most commonly performed (semi-)automatically using instance matching frameworks. However, current instance matching benchmarks fail to provide end users and developers with the necessary insights pertaining to how current frameworks behave when dealing with real data. In this paper, we present the results of the evaluation of instance matching systems using LANCE, a domain-independent, schema agnostic instance matching benchmark generator for Linked Data. LANCE is the first benchmark generator for Linked Data to support semantics-aware test cases that take into account complex OWL constructs in addition to the standard test cases related to structure and value transformations. We provide a comparative analysis with benchmarks produced using the LANCE framework for different domains to assess and identify the capabilities of state of the art instance matching systems. © 2016, CEUR-WS. All rights reserved.


Roditakis K.,Institute of Computer Science FORTH | Argyros A.A.,Institute of Computer Science FORTH | Argyros A.A.,University of Crete
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2015

Research in vision-based 3D hand tracking targets primarily the scenario in which a bare hand performs unconstrained motion in front of a camera system. Nevertheless, in several important application domains, augmenting the hand with color information so as to facilitate the tracking process constitutes an acceptable alternative. With this observation in mind, in this work we propose a modification of a state of the art method [12] for markerless 3D hand tracking, that takes advantage of the richer observations resulting from a colored glove. We do so by modifying the 3D hand model employed in the aforementioned hypothesize-and-test method as well as the objective function that is minimized in its optimization step. Quantitative and qualitative results obtained from a comparative evaluation of the baseline method to the proposed approach confirm that the latter achieves a remarkable increase in tracking accuracy and robustness and, at the same time, reduces drastically the associated computational costs. © Springer International Publishing Switzerland 2015.


Papakonstantinou V.,Institute of Computer Science FORTH | Flouris G.,Institute of Computer Science FORTH | Fundulaki I.,Institute of Computer Science FORTH | Stefanidis K.,University of Tampere | Roussakis G.,Institute of Computer Science FORTH
CEUR Workshop Proceedings | Year: 2016

As LOD datasets are constantly evolving, both at schema and instance level, there is a need for systems that support efficiently storing and querying such evolving data. The aim of this paper is to describe the way that such RDF archiving systems could be evaluated by presenting the different benchmarks in the literature, as long as the state-of-the-art archiving systems that currently exist. In addition, the weak points of such benchmarks are mentioned, and a blueprint is provided on how we are willing to deal with them. © 2016, CEUR-WS. All rights reserved.


Saveta T.,Institute of Computer Science FORTH | Daskalaki E.,Institute of Computer Science FORTH | Flouris G.,Institute of Computer Science FORTH | Fundulaki I.,Institute of Computer Science FORTH | Ngomo A.-C.N.,University of Leipzig
CEUR Workshop Proceedings | Year: 2015

Identifying duplicate instances in the Data Web is most commonly performed (semi-)automatically using instance matching frameworks. However, current instance matching benchmarks fail to provide end users and developers with the necessary insights pertaining to how current frameworks behave when dealing with real data. In this demo paper, we present lance, a domain-independent instance matching benchmark generator for Linked Data. lance is the first benchmark generator for Linked Data to support semantics-aware test cases that take into account complex OWL constructs in addition to the standard test cases related to structure and value transformations. lance supports the definition of matching tasks with varying degrees of difficulty and produces a weighted gold standard, which allows a more fine-grained analysis of the performance of instance matching tools. It can accept as input any linked dataset and its accompanying schema to produce a target dataset implementing test cases of varying levels of difficulty. In this demo, we will present the benchmark generation process underlying lance as well as the user interface designed to support lance users.


Milioris D.,University of Crete | Milioris D.,University Paris - Sud | Milioris D.,French Institute for Research in Computer Science and Automation | Tzagkarakis G.,CEA Saclay Nuclear Research Center | And 7 more authors.
Ad Hoc Networks | Year: 2014

Accurate location awareness is of paramount importance in most ubiquitous and pervasive computing applications. Numerous solutions for indoor localization based on IEEE802.11, bluetooth, ultrasonic and vision technologies have been proposed. This paper introduces a suite of novel indoor positioning techniques utilizing signal-strength (SS) fingerprints collected from access points (APs). Our first approach employs a statistical representation of the received SS measurements by means of a multivariate Gaussian model by considering a discretized grid-like form of the indoor environment and by computing probability distribution signatures at each cell of the grid. At run time, the system compares the signature at the unknown position with the signature of each cell by using the Kullback-Leibler Divergence (KLD) between their corresponding probability densities. Our second approach applies compressive sensing (CS) to perform sparsity-based accurate indoor localization, while reducing significantly the amount of information transmitted from a wireless device, possessing limited power, storage, and processing capabilities, to a central server. The performance evaluation which was conducted at the premises of a research laboratory and an aquarium under real-life conditions, reveals that the proposed statistical fingerprinting and CS-based localization techniques achieve a substantial localization accuracy. © 2013 Elsevier B.V. All rights reserved.


Tsagkatakis G.,Institute Of Computer Science Forth | Tsakalides P.,University of Crete
IEEE Transactions on Mobile Computing | Year: 2015

Fingerprint-based location sensing technologies play an increasingly important role in pervasive computing applications due to their accuracy and minimal hardware requirements. However, typical fingerprint-based schemes implicitly assume that communication occurs over the same channel (frequency) during the training and the runtime phases. When this assumption is violated, the mismatches between training and runtime fingerprints can significantly deteriorate the localization performance. Additionally, the exhaustive calibration procedure required during training limits the scalability of this class of methods. In this work, we propose a novel, scalable, multi-channel fingerprint-based indoor localization system that employs modern mathematical concepts based on the Sparse Representations and Matrix Completion theories. The contribution of our work is threefold. First, we investigate the impact of channel changes on the fingerprint characteristics and the effects of channel mismatch on state-of-the-art localization schemes. Second, we propose a novel fingerprint collection technique that significantly reduces the calibration time, by formulating the map construction as an instance of the Matrix Completion problem. Third, we propose the use of sparse Bayesian learning to achieve accurate location estimation. Experimental evaluation on real data highlights the superior performance of the proposed framework in terms of reconstruction error and localization accuracy. © 2015 IEEE.

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