Software for computer animation is generally characterized by a steep learning curve, due to the entanglement of both sophisticated techniques and interaction methods required to control 3D geometries. This paper proposes a tool designed to support computer animation production processes by leveraging the affordances offered by articulated tangible user interfaces and motion capture retargeting solutions. To this aim, orientations of an instrumented prop are recorded together with animator's motion in the 3D space and used to quickly pose characters in the virtual environment. High-level functionalities of the animation software are made accessible via a speech interface, thus letting the user control the animation pipeline via voice commands while focusing on his or her hands and body motion. The proposed solution exploits both off-the-shelf hardware components (like the Lego Mindstorms EV3 bricks and the Microsoft Kinect, used for building the tangible device and tracking animator's skeleton) and free open-source software (like the Blender animation tool), thus representing an interesting solution also for beginners approaching the world of digital animation for the first time. Experimental results in different usage scenarios show the benefits offered by the designed interaction strategy with respect to a mouse & keyboard-based interface both for expert and non-expert users.

Due to the continuous advancements made in robot technologies, the development of intuitive and effective user interfaces for human-robot interaction is getting increasingly important. This paper investigates how different types of interfaces can be used for allowing a single operator to remotely control a team of robots endowed with different capabilities. Attention is focused on three user interfaces based on a gamepad, on a mobile device and on hand tracking, respectively. To evaluate pros and cons of the above interfaces, a user study was conducted, in which participants had to combine the capabilities of a rover, a drone and a robotic arm in order to carry out a search and pick task. Based on the experiments, the fastest way to complete the task was to use the mobile device. However, results showed that some of the interfaces could provide better performances for selected robots and associated sub-tasks. It is worth observing that, despite evidences about efficiency, participants rated the gamepad as the preferred interface from the point of view of subjective usability.

This paper presents a framework for automatically generating speech-based interfaces for controlling virtual and augmented reality (AR) applications on wearable devices. Starting from a set of natural language descriptions of application functionalities and a catalog of general-purpose icons, annotated with possible implied meanings, the framework creates both vocabulary and grammar for the speech recognizer, as well as a graphic interface for the target application, where icons are expected to be capable of evoking available commands. To minimize user's cognitive load during interaction, a semantics-based optimization mechanism was used to find the best mapping between icons and functionalities and to expand the set of valid commands. The framework was evaluated by using it with see-through glasses for AR-based maintenance and repair operations. A set of experimental tests were designed to objectively and subjectively assess first-time user experience of the automatically generated interface in relation to that of a fully personalized interface. Moreover, intuitiveness of the automatically generated interface was studied by analyzing the results obtained through trained users on the same interface. Objective measurements (in terms of false positives, false negatives, task completion rate, and average number of attempts for activating functionalities) and subjective measurements (about system response accuracy, likeability, cognitive demand, annoyance, habitability, and speed) reveal that the results obtained by the first-time users and experienced users with the proposed framework's interface are very similar, and their performances are comparable with those of both the considered references.

The players of the digital industry look at network Big Data as an incredible source of revenues, which can allow them to design products, services, and market strategies ever more tailored to users' interests and needs. This is the case of data collected by Web analytics tools, which describe the way users interact with Web contents and where their attention focuses onto during navigation. Given the complexity of information to analyze, existing tools often make use of visualization strategies to represent data aggregated throughout separate sessions and multiple users. In particular, heat maps are often adopted to study the distribution of mouse activity and identify page regions that are more frequently reached during interaction. Unfortunately, since Web contents are accessed via ever more heterogeneous devices, region-based heat maps cannot be exploited anymore to aggregate data concerning user's attention, since the same Web content may move to another page location or exhibit a different aspect depending on the access device used or the user agent setup. This paper presents the design of a visual analytics framework capable to deal with the above limitation by adopting a data collection approach that combines information about regions displayed with information about page structure. This way, the well-known heat map-based visualization can be produced, where interactions can be aggregated on a per-element basis independently of the specific access configuration. Experimental results showed that the framework succeeds in accurately quantifying user's attention and replicating results obtained by manual processing.

The ability to plan a training path fulfilling working needs plays a key role, when it comes to finding the desired job. Nonetheless, in a dynamic scenario characterized by frequent technological innovations and economic changes, as the present one, getting a clear picture of the requirements of the world of work could become difficult, as it would imply performing a deep periodic evaluation of available job offers, and matching them with own previous experience. Intelligent systems able to automatically match résumés with job offers already exist, but they are mostly targeted to recruiters, instead of learners. This work aims at filling this gap, by presenting a system for comparing learners' acquirements - expressed in terms of learning outcomes - with companies requirements. The proposed system relies on semantics for processing natural language texts and exploits bar charts to visualize learning outcomes and their levels in order to quickly depict similarities and differences between résumés and job offers. An evaluation, on 50 volunteers, underlined the added value of the system.

Drone-based delivery of goods could become a reality in the near future, as witnessed by the increasing successful experiences in both research and commercial fields. In this paper, a prototype system exploiting a do it yourself quad copter drone for delivering products is proposed. On the one hand, the hardware choices made in order to limit risks arising from autonomous delivery are presented. On the other hand, a framework for orders placement and shipment is shown. The advantages of a system like the one described in this paper are mainly related to an increased delivery speed, especially in urban contexts with traffic, to the possibility to make deliveries in areas usually difficult to be reached, and to the drone's ability to autonomously carry out consignments. A practical use case, in which the proposed system is used for delivering drugs (an application in which the need to quickly receive the good might be particularly important) is shown. Nevertheless, the proposed prototype could be employed in other contexts, such as take-away deliveries, product shipments, registered mail consignments, etc.

The fast pace of change in high technology product development requires high flexibility and adaptation in product design and technology management. Within this context, appropriate learning environments are planned to shape future Information and Communication Technology staff in such a way they could get able to keep up with new trends and innovations. A constructivist educational model based on Active Learning represents a step towards this vision for staff training. This paper deals with a new organization of a 13 weeks course based on the inverted class model, where students are introduced to the problem-posing domain by a direct involvement of enterprises, which participate specifically to the problem definition and assessment phases. In the case study shown, students simulate a production business unit in all the phases that characterize the product/service development life cycle. A mixed traditional and company-based organization of the course is supposed to improve students competences by proposing solutions leveraging on appropriate tools taken from traditional experiences and also gaining benefits from a learning-by-doing approach that encourages the use of state-of-the-art solutions. A preliminary study is performed to see how expected professional competences are met during the course, with the additional goal of collecting indications that could help to improve the learning framework for future editions of the course.

Existing tracking methods designed for interacting with projection-based displays generally require visible artifacts to be introduced in the environment in order to guarantee effective stability and accuracy. For instance, in optical-oriented approaches, either the camera sensor or the reference pattern used for tracking are often located within the user's sight (or interfere with it), thus occluding portions of the scene or altering the perception of the virtual environment. Several ways to tackle these issues have been recently explored. Proposed approaches basically aim at making the presence of tracking references in the virtual space transparent to the user. However, such solutions introduce possibly critical constraints on required hardware or environment configuration. In this work, a novel tracking approach based on imperceptible fiducial markers is proposed. The approach relies on a hiding technique that allows digital images to be embedded in (and retrieved from) a projected scene by exploiting the properties of light polarization and additive color mixing. In particular, the virtual scene is obtained by overlapping the light beams of two projectors and by dealing with markers’ hiding via color compensation. A prototype setup has been deployed, where interaction with a flat surface projection environment has been evaluated in terms of tracking accuracy and artifacts avoidance performance by using a consumer camera equipped with a polarizing filter. Although the performed tests presented in this article represent only a preliminary and a partial evaluation of the proposed approach, they provided encouraging results indicating that the proposed technique could be possibly applied in more complex interaction scenarios still with limited hardware requirements.

The modular exponentiation on large numbers is computationally intensive. An effective way for performing this operation consists in using Montgomery exponentiation in the Residue Number System (RNS). This paper presents an algorithmic and architectural study of such exponentiation approach. From the algorithmic point of view, new and state-of-the-art opportunities that come from the reorganization of operations and precomputations are considered. From the architectural perspective, the design opportunities offered by well-known computer arithmetic techniques are studied, with the aim of developing an efficient arithmetic cell architecture. Furthermore, since the use of efficient RNS bases with a low Hamming weight are being considered with ever more interest, four additional cell architectures specifically tailored to these bases are developed and the tradeoff between benefits and drawbacks is carefully explored. An overall comparison among all the considered algorithmic approaches and cell architectures is presented, with the aim of providing the reader with an extensive overview of the Montgomery exponentiation opportunities in RNS.

A wide variety of remotely controlled mobile robots have been developed in the past. Despite the evolution in robotics functionalities, not much effort has been made to customize control interfaces to meet user preferences and needs. General users can find difficulties in controlling mobile robots by standard interfaces. On the other hand, recent advances in multi-touch devices now allow researchers to design and implement intuitive and user friendly interfaces. This paper presents recent efforts to control mobile platforms by using customizable multi-touch gestures drawn on a commonly available hand-held device. In particular, a live stream video coming from an on board camera allows both to automatically and manually control the platform to accomplish target tracking and following tasks.

Abstract Shared visualization environments represent an effective means to enhance collaborative work in engineering and scientific design tasks. The availability of high‐speed networks allows researchers to work together from geographically distributed locations, and mobile devices are able to carry out ubiquitous 3D visualization tasks through wireless network connections. This paper presents a scalable architecture for the delivery of shared 3D visualization services to heterogeneous terminals ranging from powerful workstations to mobile devices such as PDAs and smart‐phones. The framework design allows both desktop and mobile clients to simultaneously visualize the same model by sharing a common view. Remote‐rendering servers support effective visualization on thin clients, and a load balancing mechanism provides efficient resource usage. Copyright © 2011 John Wiley & Sons, Ltd.