This study presents the validation of the Greek translation of the Godspeed Questionnaire Series (GQS) through an interactive workshop. Data were collected from 94 participants who assessed seven different wearable robotic devices in person and online. The validation examined psychometric characteristics including internal consistency reliability and factor structure preservation. The Greek translation successfully adapted semantic differentials while achieving equivalent performance compared to the original English version. Cross-language equivalence was maintained across most dimensions. All subscales demonstrated significant discriminative ability between robotic devices, validating the Greek GQS as a reliable instrument for human-robot interaction research and user assessment in wearable robotics applications.

The recognition of Steady State Visual Evoked Potentials (SSVEP) constitutes a challenging problem in Brain Computer Interfaces (BCI), especially when the number of EEG sensors is limited. In this work, we propose a new sparse representation classification scheme that extends current schemes by exploiting the graph properties of relevant features. Based on this scheme each test signal is represented as a linear combination of train signals. Our expectation is that this constrained linear combination, exploiting the graph's structure of the training data, will lead to representations that are more robust. Moreover, in order to avoid overfitting and provide a model with good generalization abilities we adopt the bayesian framework and, in particular, the Variational Bayesian Framework since we use a specific prior distribution to exploit the graph structure of the data. The proposed algorithm has been evaluated on two SSVEP datasets achieving state-of- the-art performance against well known classification methods in SSVEP literature.

Self-similarity is a prominent characteristic of natural images that can play a major role when it comes to their denoising, restoration or compression. In this paper, we propose a novel probabilistic model that is based on the concept of image patch similarity and applied to the problem of Single Image Super Resolution. Based on this model, we derive a Variational Bayes algorithm, which super resolves low-resolution images, where the assumed distribution for the quantified similarity between two image patches is heavy-tailed. Moreover, we prove mathematically that the proposed algorithm is both an extended and superior version of the probabilistic Non-Local Means (NLM). Its prime advantage remains though, which is that it requires no training. A comparison of the proposed approach with state-of-the-art methods, using various quantitative metrics shows that it is almost on par, for images depicting rural themes and in terms of the Structural Similarity Index (SSIM) with the best performing methods that rely on trained deep learning models. On the other hand, it is clearly inferior to them, for urban themed images and in terms of all metrics, especially for the Mean-Squared-Error (MSE). In addition, qualitative evaluation of the proposed approach is performed using the Perceptual Index metric, which has been introduced to better mimic the human perception of the image quality. This evaluation favors our approach when compared to the best performing method that requires no training, even if they perform equally in qualitative terms, reinforcing the argument that MSE is not always an accurate metric for image quality.

The recognition of Steady State Visual Evoked Responses (SSVEP) is a problem that arises frequently into the Brain Computer Interface (BCI) concept. In this paper, we present a novel algorithm for their recognition by formulating the pattern recognition problem in terms of linear regression. The inverse problem is solved using the bayesian framework and the decision is ruled in favor of the class with the minimum distance. The experimental results demonstrate that the proposed algorithm achieves better performance, in terms of classification accuracy, than the competing approaches.

A significant limitation on the wide use of a Motor Imagery (MI) Brain-Computer Interfaces is the acquisition of electroenchephalogram (EEG) data over a significant amount of EEG trials to achieve accurate classification. In this work, we propose a new sparse representation classification scheme to overcome the above limitation by training the proposed model using only a limited amount of EEG trials. Our algorithm extends current sparse representation classification schemes by exploiting the group sparsity of EEG features. We have evaluated the proposed algorithm on two MI EEG datasets, showing state-of-the-art performance against well known classification methods of MI BCI literature.

This paper deals with the classification of steady-state visual evoked potentials (SSVEP), which is a multiclass classification problem addressed in SSVEP-based brain–computer interfaces. In particular, our method named MultiLRM_MKL uses multiple linear regression models under a Sparse Bayesian Learning (SBL) framework to discriminate between the SSVEP classes. The regression coefficients of each model are learned using the Variational Bayesian (VB) framework and the kernel trick is adopted not only for reducing the computational cost of our method, but also for enabling the combination of different kernel spaces. We verify the effectiveness of our method in handling different kernel spaces by evaluating its performance with a new kernel based on canonical correlation analysis. In particular, we prove the benefit of combining multiple kernels by outperforming several state-of-the-art methods in two SSVEP datasets, reaching an information transfer rate of 93 b/min using only three channels from the occipital area ($O_z$,$O_1$, and$O_2$).

Error-Related Potentials (ErrPs) have been used lately in order to improve several existing Brain-Computer Interface (BCI) applications. In our study we investigate the contribution of ErrPs in a Steady State Visual Evoked Potential (SSVEP) based BCI. An extensive study is presented in order to discover the limitations of the proposed scheme. Using Common Spatial Patterns and Random Forests we manage to show encouraging results regarding the incorporation of ErrPs in a SSVEP system. Finally, we provide a novel methodology (Inverse Correct Response Time) that can measure the gain of a BCI system by incorporating ErrPs in terms of time efficiency.

Brain-computer interfaces (BCIs) have been gaining momentum in making human-computer interaction more natural, especially for people with neuro-muscular disabilities. Among the existing solutions the systems relying on electroencephalograms (EEG) occupy the most prominent place due to their non-invasiveness. In this work, we provide a review of various existing techniques for the identification of motor imagery (MI) tasks. More specifically, we perform a comparison between Common Spatial Patterns (CSP) related features and features based on Power Spectral Density (PSD) techniques. Furthermore, for the identification of MI tasks, two well-known classifiers are used, the Linear Discriminant Analysis (LDA) and the Support Vector Machines (SVM). Our results confirm that PSD features demonstrate the most consistent robustness and effectiveness in extracting patterns for accurately discriminating between left and right MI tasks.

In this paper, we present SALIC, an active learning method for selecting the most appropriate user tagged images to expand the training set of a binary classifier. The process of active learning can be fully automated in this social context by replacing the human oracle with the images' tags. However, their noisy nature adds further complexity to the sample selection process since, apart from the images' informativeness (i.e., how much they are expected to inform the classifier if we knew their label), our confidence about their actual label should also be maximized (i.e., how certain the oracle is on the images' true contents). The main contribution of this work is in proposing a probabilistic approach for jointly maximizing the two aforementioned quantities. In the examined noisy context, the oracle's confidence is necessary to provide a contextual-based indication of the images' true contents, while the samples' informativeness is required to reduce the computational complexity and minimize the mistakes of the unreliable oracle. To prove this, first, we show that SALIC allows us to select training data as effectively as typical active learning, without the cost of manual annotation. Finally, we argue that the speed-up achieved when learning actively in this social context (where labels can be obtained without the cost of human annotation) is necessary to cope with the continuously growing requirements of large-scale applications. In this respect, we demonstrate that SALIC requires ten times less training data in order to reach the same performance as a straightforward informativeness-agnostic learning approach.

The rise of big data, which need computationally demanding manipulation has posed unprecedented challenges in the machine learning community. In this context, a variety of dimensionality reduction methods has been introduced in order to deal with the large-scale aspect of the data. However, their employment in very large scales often becomes impractical due to memory and computation limitations. In parallel, Compressive Sampling (CS) has recently emerged as a powerful mathematical framework providing a suite of conditions and methods that allow for an almost lossless and efficient compression of sparse data. Given that the majority of big data problems entail the existence of sparse datasets, our goal in this paper is to investigate the potential of CS as a dimensionality reduction method in very large scales. Towards this end, we propose a novel Product Compressive Sampling (PCS) method that is used for scalable image annotation. The new method displays robustness equal to the typical CS method, while decreases the computational complexity dramatically. Another novel characteristic of our work consists in establishing a connection between the sparsity level of the data and the effectiveness of PCS as a dimensionality reduction method for image annotation. For this purpose, a new metric for estimating the data sparsity is proposed. Finally, in comparison with the state-of-the-art, we show that PCS displays competitive classification performance, while at the same moment proves to be orders of magnitude superior in terms of computational efficiency.

In this paper we present FastAR, a software component capable of transforming Joomla based websites into AR-channels compatible with the most popular augmented reality browsers (i.e. Junaio, Layar, Wikitude). FastAR exploits the consistency of the data structure across multiple sites that have been developed using the same content management system, so as to automate the transformation process of an internet website to an augmented reality channel. The proposed component abstracts all related programming tasks and significantly reduces the time required to generate and publish AR-content, making the entire process manageable by non-experts. In verifying the usefulness and effectiveness of FastAR, we conducted a survey to solicit the opinion of users who carried out the installation and transformation process.

Motivated by the recently introduced scalable concept detection challenge that requires classifiers for hundreds or even thousands of concepts, the objective of this work is to predict the cases where the enhancement of an initial classifier with additional training images is not expected to provide significant improvements. To facilitate this objective, we need a model for predicting the performance gain of a bootstrapping process prior to actually applying it. In order to train this model, we propose two features; the initial classifier's maturity (i.e. how close is the current hyperplane to the optimal) and the oracle's reliability (i.e. how reliable is the oracle in providing the correct labels of new training data). Thus, the contribution of our work is on proposing a method that is able to exploit the correlation between the expected performance boost and these two indicators. As a result, we can considerably improve the scalability properties of such bootstrapping processes by concentrating on the most prominent models and thus reducing the overall processing load.

ImproveMyCity is an open source platform that enables residents to directly report to their public administration local issues about their neighborhood such as discarded trash bins, faulty street lights, broken tiles on sidewalks, illegal advertising boards, etc. The reported issues are automatically transmitted to the appropriate office in public administration so as to schedule their settlement. Reporting is feasible both through a web- and a smartphone-based front-end that adopt a map-based visualization, which makes reporting a user-friendly and intriguing process. The management and routing of incoming issues is performed through a back-end infrastructure that serves as an integrated management system with easy to use interfaces. Apart from reporting a new issue, both front-ends allow the citizens to add comments or vote on existing issues, which adds a social dimension on the collected content. Finally, the platform makes also provision for informing the citizens about the progress status of the reported issue and in this way facilitate the establishment of a two-way dialogue between the citizen and public administration.

Our purpose in this work is to boost the performance of object classifiers learned using the self-training paradigm. We exploit the multi-modal nature of tagged images found in social networks, to optimize the process of region selection when retraining the initial model. More specifically, the proposed approach uses a small number of manually labelled regions to train the initial object detection classifiers. Then, a large number of loosely tagged images, pre-segmented by an automatic segmentation algorithm, is used to enhance the initial training set with additional image regions. However, in contrast to the typical case of self-training where the image regions are selected based solely on how well they fit to the original classification model, our approach aims at optimizing this selection by making combined use of both visual and textual information. The experimental results show that the object detection classifiers generated using the proposed approach outperform the classifiers generated using the typical self-training paradigm.

Computer vision techniques have made considerable progress in recognizing object categories by learning models that normally rely on a set of discriminative features. However, in contrast to human perception that makes extensive use of logic-based rules, these models fail to benefit from knowledge that is explicitly provided. In this paper, we propose a framework that can perform knowledge-assisted analysis of visual content. We use ontologies to model the domain knowledge and a set of conditional probabilities to model the application context. Then, a Bayesian network is used for integrating statistical and explicit knowledge and performing hypothesis testing using evidence-driven probabilistic inference. In addition, we propose the use of a focus-of-attention (FoA) mechanism that is based on the mutual information between concepts. This mechanism selects the most prominent hypotheses to be verified/tested by the BN, hence removing the need to exhaustively test all possible combinations of the hypotheses set. We experimentally evaluate our framework using content from three domains and for the following three tasks: 1) image categorization; 2) localized region labeling; and 3) weak annotation of video shot keyframes. The results obtained demonstrate the improvement in performance compared to a set of baseline concept classifiers that are not aware of any context or domain knowledge. Finally, we also demonstrate the ability of the proposed FoA mechanism to significantly reduce the computational cost of visual inference while obtaining results comparable to the exhaustive case.

In this paper, we present a novel framework for machine learning-based cross-media knowledge extraction. The framework is specifically designed to handle documents composed of three types of media -- text, images and raw data -- and to exploit the evidence for an extracted fact from across the media. We validate the framework by applying it in the design and development of cross-media extraction systems in the context of two real-world use cases in the car manufacturing industry. Moreover, we show that in these use cases the cross-media approach effectively improves system extraction accuracy.

In large organizations the resources needed to solve challenging problems are typically dispersed over systems within and beyond the organization, and also in different media. However, there is still the need, in knowledge environments, for extraction methods able to combine evidence for a fact from across different media. In many cases the whole is more than the sum of its parts: only when considering the different media simultaneously can enough evidence be obtained to derive facts otherwise inaccessible to the knowledge worker via traditional methods that work on each single medium separately. In this paper, we present a cross-media knowledge extraction framework specifically designed to handle large volumes of documents composed of three types of media text, images and raw data and to exploit the evidence across the media. Our goal is to improve the quality and depth of automatically extracted knowledge.

Replica detection is a prerequisite for the discovery of copyright infringement and detection of illicit content. For this purpose, content-based systems can be an efficient alternative to watermarking. Rather than imperceptibly embedding a signal, content-based systems rely-on image similarity. Certain content-based systems use adaptive classifiers to detect replicas. In such systems, a suspect image is tested against every original, which can become computationally prohibitive as the number of original images grows. In this paper, we propose using R-tree indexing to decrease the necessary number of comparisons and rapidly select the most likely originals. Experimental results show that the proposed system performs very satisfactorily and that up to 99.3% of the originals can be discarded before applying the binary classifiers.

In this paper a novel system for image replica detection is presented. The system uses color-based descriptors in order to extract robust features for image representation. These features are used for indexing the images in a database using an R-tree. When a query about whether a test image is a replica of an image in the database is submitted, the R-tree is traversed and a set of candidate images is retrieved. Then, in order to obtain a single result and at the same time reduce the number of decision errors the system is enhanced with linear discriminant analysis (LDA). The conducted experiments show that the proposed approach is very promising