Engagement in the companies' training programs is a crucial issue for their success. Every company faces the matter in different manners: some enterprises promote mandatory courses or programs; however, they may result not enough effective, due to a low level of motivation that can compromise learning and retention. Other enterprises prefer to adopt voluntary courses or programs, but in this case enrollment and completion depend on many factors: the time that workers can spend on training without compromising their main tasks; the directives of managers; the individual motivation. Another way to face corporate training may exploit gamification to enhance engagement. The authors present the design and implementation of a gamified course of office automation, developed for the employees of a large media company. Our approach attempts to go beyond the well-known triad point-badges-leaderboards, employing also a narrative, the Bartle's taxonomy of player types and the Self-determination theory by Deci and Ryan.
Observations of human grasping [7] [6] have shown two phases: during the reaching phase of grasping, the hand preshapes in order to prepare the 'shape matching" with the object to grasp, that is the following adjusting phase. Planning grasping with dextrous robotics hands can not be summarized to these two phases. We have to split the grasping process into several phases (frequently overlapped), and also we have to look at some arising problems such as: (1) object recognition ( 2 ) planning accessibility, (3) task planning, (4) initial touch and grab phase, (5) stable grasp phase, which are consciously or unconsciously generated by a human being. A major issue addressed in this work is to integrate a part of these components in the preshaping, and in particular, the automatic planning accessibility and preshaping of objects to be grasped. The system is based on 2d analysis of slices extracted from an octree representation of objects.
Dynamic programming is a popular optimization technique, developed in the 60’s and still widely used today in several fields for its ability to find global optimum. Dynamic Programming Algorithms (DPAs) can be developed in many dimension. However, it is known that if the DPA dimension is greater or equal to two, the algorithm is an NP complete problem. In this paper we present an approximation of the fully two- dimensional DPA (2D-DPA) with polynomial complexity. Then, we describe an implementation of the algorithm on a recent parallel device based on CUDA architecture. We show that our parallel implementation presents a speed-up of about 25 with\ud
respect to a sequential implementation on an Intel I7 CPU.\ud
In particular, our system allows a speed of about ten 2D-DPA executions per second for 85 × 85 pixels images. In the experimental Section of the paper we report some image warping examples performed with our CUDA-based 2D-DPA and speed-up figures
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