Successful transition of manufacturing enterprises to Industrie 4.0 (I4.0) is highly dependent on the adoption and integration of new technologies, toward making manufacturing processes even smarter and less wasteful. In this paper, we present the OPTIMAI project architecture for zero-defect manufacturing (ZDM), applicable to a variety of industrial verticals. To realise a standards-based approach, we elaborate on the parallels drawn between the presented architectural framework and two leading reference architectures underpinning the "factories of the future" vision (RAMI 4.0 and IIRA). System specifications for ZDM are hence defined according to the perspectives of the two architectural models, allowing us to examine cutting-edge technologies for ZDM (such as blockchain, AI and AR) as both an I4.0 solution, as well as an Industrial Internet of Things system.

In this paper, we address the problem of recognizing student engagement in prosocial games by exploiting engagement cues from different input modalities. Since engagement is a multifaceted phenomenon with different dimensions, i.e., behavioral, cognitive, and affective, we propose the modeling of student engagement using real-time data from both the students and the game. More specifically, we apply body motion and facial expression analysis to identify the affective state of students, while we extract features related to their cognitive and behavioral engagement based on the analysis of their interaction with the game. For the automatic recognition of engagement, we adopt a machine learning approach based on artificial neural networks, while for the annotation of the engagement data, we introduce a novel approach based on the use of games with different degrees of challenge in conjunction with a retrospective self-reporting method. To evaluate the proposed methodology, we conducted real-life experiments in 4 classes, in 3 primary schools, with 72 students and 144 gameplay recordings in total. Experimental results show the great potential of the proposed methodology, which improves the classification accuracy of the three distinct dimensions with a detection rate of 85%. A detailed analysis of the role of each component of the Game Engagement Questionnaire, i.e., immersion, presence, flow, and absorption, in the classification process is also presented in this paper.

Tele-immersion (TI) related technologies can change the way people interact and bridge the gap between the physical and digital worlds. However, while the technology itself advances, most developed platforms have complex setups and require large investments. In this work, a low-cost platform is introduced, integrating multiple TI-related advances. Focusing on ease of use and rapid deployment, a fast and fully automatic calibration method is proposed. The platform enables real-time 3D reconstruction of users and their placement into a pre-authored 3D environment. Moreover, interaction is achieved through the user's body posture, removing the need for additional equipment and enabling natural control while immersed. Developing a real-time TI platform requires the efficient integration of several multidisciplinary elements. An elegant, minimal solution to these challenges is proposed and validated in a prototype TI multiplayer game, SpaceWars.

In this paper, a gaze-based Relevance Feedback (RF) approach to region-based image retrieval is presented. Fundamental idea of the proposed method comprises the iterative estimation of the real-world objects (or their constituent parts) that are of interest to the user and the subsequent exploitation of this information for refining the image retrieval results. Primary novelties of this work are: a) the introduction of a new set of gaze features for realizing user's relevance assessment prediction at region-level, and b) the design of a time-efficient and effective object-based RF framework for image retrieval. Regarding the interpretation of the gaze signal, a novel set of features is introduced by formalizing the problem under a mathematical perspective, contrary to the exclusive use of explicitly defined features that are in principle derived from the psychology domain. Apart from the temporal attributes, the proposed features also represent the spatial characteristics of the gaze signal, which have not been extensively studied in the literature so far. On the other hand, the developed object-based RF mechanism aims at overcoming the main limitation of region-based RF approaches, i.e., the frequently inaccurate estimation of the regions of interest in the retrieved images. Moreover, the incorporation of a single-camera image processing-based gaze tracker makes the overall system cost efficient and portable. As it is shown by the experimental evaluation, the proposed method outperforms representative global- and region-based explicit RF approaches, using a challenging general-purpose image dataset.