Blind robust watermarking schemes for copyright protection of 3D mesh objects

In this paper, two novel methods suitable for blind 3D mesh object watermarking applications are proposed. The first method is robust against 3D rotation, translation, and uniform scaling. The second one is robust against both geometric and mesh simplification attacks. A pseudorandom watermarking signal is cast in the 3D mesh object by deforming its vertices geometrically, without altering the vertex topology. Prior to watermark embedding and detection, the object is rotated and translated so that its center of mass and its principal component coincide with the origin and the z-axis of the Cartesian coordinate system. This geometrical transformation ensures watermark robustness to translation and rotation. Robustness to uniform scaling is achieved by restricting the vertex deformations to occur only along the r coordinate of the corresponding (r, /spl theta/, /spl phi/) spherical coordinate system. In the first method, a set of vertices that correspond to specific angles /spl theta/ is used for watermark embedding. In the second method, the samples of the watermark sequence are embedded in a set of vertices that correspond to a range of angles in the /spl theta/ domain in order to achieve robustness against mesh simplifications. Experimental results indicate the ability of the proposed method to deal with the aforementioned attacks.     

A review of current research in manufacturing shop design integration

The importance of the manufacturing shop design in the successful operation of a production system is well documented and, as a result, significant work has been devoted to this area. Due to the inherent complexity of the global shop design problem, much research has focused on individual sub-problems, including the layout of manufacturing resources, the design of the shop aisle network, and the selection of the material handling equipment, rather than addressing the integrated problem directly. This paper reviews literature contributions to shop design integration, as well as partial integration schemes such as the design for operation and control of material handling systems. Furthermore, it examines the ability of models and solution approaches for individual sub-problems of shop design to support integration schemes.     

The impact of computer self-efficacy,situational interest and academic self-concept in virtual communities of inquiry during the distance learning procedures through Second Life

The current study investigates a case where the online learning procedure in three-dimensional (3D) technologically-advanced environments of the Web 2.0 is growing at an exponential rate. In this occasion it is highly imperative need to understand students’ interactions in this innovative mode of e-Education that requires from educators and scholars not only analysis conceptually, but also an empirically-driven optimization. The community of inquiry (CoI) model (or framework) consists to be as one of the most prominent multi-dimensional constructs that it is widely used to represent several distinct dimensions of social presence, teaching presence and cognitive presence, as a unique and fundamental theoretical concept to measure students’ interactions in contemporary electronic environments. Although, the effectiveness of these multi-dimensional constructs creates a dilemma to researchers who want the breadth and comprehensiveness of this model for the precision and clarity of users’ (instructors and students) dimensions with other motivational and learning variables. To address this dilemma, the current empirical study presents statistical analyses from the “trinity” constructs of the CoI model by utilizing correlation and hierarchical regression analyses with two fundamental motivational (computer self-efficacy and situational interest) and another one learning (academic self-concept) variables. This study goes one step further and introduces the conspicuously indisputable intervention of a virtual (V)CoI and its utilization in multi-user virtual worlds, like Second Life (SL). The study findings of one hundred thirty-five (135) participants who enrolled in several online sessions unveiled that the situational interest was the only significant predictor of social presence. The computer self-efficacy was not a significant predictor of the CoI model, while on the other hand academic self-concept was a significant predictor in a revamped attempt to validate the strong relationship among constructs within it. According to the aforementioned reasons, it can be surmised that the successful combination of the VCoI in Second Life, surpassing irrefutable and inherent shortcomings to a future-driven sustainable use and growth.     

Enabling customers engagement and collaboration for small and medium-sized enterprises in ubiquitous multi-channel ecosystems

Over the last few years, we have encountered an exponential growth in online communication opportunities. Organizations have more and more ways to connect and engage with their current or future customers. The existence of more opportunities in connecting to people can be both an enabler and a burden. Being present at a multitude of different channels requires the effective management of a very large number of adapted contents, formats, and interaction patterns fulfilling the communication and cooperation needs of distributed target groups. In this respect, we integrate existing fragmented communication and monitoring approaches into a full-fledged communication model as a basis for an adequate engagement approach. We describe applications of our approach in both the eTourism and manufacturing domain. In this paper, we introduce an approach that will enable communication, collaboration and value exchange of users through a multitude of online interaction possibilities based on the use of semantic technology. Finally, we also compare our approach with existing solutions with respect to the identified challenges in this subject.     

Dropout prediction in e-learning courses through the combination of machine learning techniques

In this paper, a dropout prediction method for e-learning courses, based on three popular machine learning techniques and detailed student data, is proposed. The machine learning techniques used are feed-forward neural networks, support vector machines and probabilistic ensemble simplified fuzzy ARTMAP. Since a single technique may fail to accurately classify some e-learning students, whereas another may succeed, three decision schemes, which combine in different ways the results of the three machine learning techniques, were also tested. The method was examined in terms of overall accuracy, sensitivity and precision and its results were found to be significantly better than those reported in relevant literature.     

CMMN evaluation: the modelers’ perceptions of the main notation elements

Software and Systems Modeling - Case Management Model and Notation (CMMN) has been introduced as a graphical modeling language targeting the modeling of human-centric processes. Despite its growing...     

Smart Workplaces for older adults: coping ‘ethically’ with technology pervasiveness

Universal Access in the Information Society - Pervasive technologies such as Artificial Intelligence, Virtual Reality and the Internet of Things, despite their great potential for improved...     

Efficient sensitivity computations in 3D air quality models

The prediction of ground level ozone for air quality monitoring and assessment is simulated through an integrated system of gridded models (meteorological, photochemical), where the atmosphere is represented with a three-dimensional grid that may include thousands of grid cells. The continuity equation solved by the Photochemical Air Quality Model (PAQM) reproduces the atmospheric processes (dynamical, physical, chemical and radiative), such as moving and mixing air parcels from one grid cell to another, calculating chemical reactions, injecting new emissions. The whole modeling procedure includes several sources of uncertainty, especially in the large data sets that describe the status of the domain (boundary conditions, emissions, chemical reaction rates and several others). The robustness of the photochemical simulation is addressed in this work through the deterministic approach of sensitivity analysis. The automatic differentiation tool ADIFOR is applied on the 3D PAQM CAMx and augments its Fortran 77 code by introducing new lines of code that additionally calculate, in only one run, the gradient of the solution vector with respect to its input parameters. The applicability of the approach is evaluated through a sensitivity study of the modeled concentrations to perturbations at the boundary conditions and the emissions, for three essentially dissimilar European Metropolises of the Auto-Oil II programme (Athens, Milan, and London).     

Speed Scaling for Maximum Lateness

We consider the power-aware problem of scheduling non-preemptively a set of jobs on a single speed-scalable processor so as to minimize the maximum lateness, under a given budget of energy. In the offline setting, our main contribution is a combinatorial polynomial time algorithm for the case in which the jobs have common release dates. In the presence of arbitrary release dates, we show that the problem becomes strongly \(\mathcal {N}\mathcal {P}\)-hard. Moreover, we show that there is no O(1)-competitive deterministic algorithm for the online setting in which the jobs arrive over time. Then, we turn our attention to an aggregated variant of the problem, where the objective is to find a schedule minimizing a linear combination of maximum lateness and energy. As we show, our results for the budget variant can be adapted to derive a similar polynomial time algorithm and an \(\mathcal {N}\mathcal {P}\)-hardness proof for the aggregated variant in the offline setting, with common and arbitrary release dates respectively. More interestingly, for the online case, we propose a 2-competitive algorithm.