Abstract
Virtual Reality (VR) is again in the spotlight. However, interactions and modeling operations are still major hurdles to its complete success. To make VR Interaction viable, many have proposed mid-air techniques because of their naturalness and resemblance to physical world operations. Still, natural mid-air metaphors for Constructive Solid Geometry (CSG) are still elusive. This is unfortunate, because CSG is a powerful enabler for more complex modeling tasks, allowing to create complex objects from simple ones via Boolean operations. Moreover, Head-Mounted Displays occlude the real self, and make it difficult for users to be aware of their relationship to the virtual environment. In this paper we propose two new techniques to achieve Boolean operations between two objects in VR. One is based on direct-manipulation via gestures while the other uses menus. We conducted a preliminary evaluation of these techniques. Due to tracking limitations, results allowed no significant conclusions to be drawn. To account for self-representation, we compared full-body avatar against an iconic cursor depiction of users' hands. In this matter, the simplified hands-only representation improved efficiency in CSG modelling tasks.

Abstract
Selecting objects outside user's arm-reach in Virtual Reality still poses significant challenges. Techniques proposed to overcome such limitations often follow arm-extension metaphors or favor the use of selection volumes combined with ray-casting. Nonetheless, these approaches work for room sized and sparse environments, and they do not scale to larger scenarios with many objects. We introduce PRECIOUS, a novel mid-air technique for selecting out-of-reach objects. It employs an iterative progressive refinement, using cone-casting to select multiple objects and moving users closer to them in each step, allowing accurate selections. A user evaluation showed that PRECIOUS compares favorably against existing approaches, being the most versatile.

Abstract

Abstract
Social network analysis (SNA) is a widely studied research topics, which has been increasingly being applied for solving different kind of problems, including industrial manufacturing ones. This paper focuses on the application of SNA on an industrial plant layout problem. The study aims at analyzing the importance of using SNA techniques to analyze important relations between entities in a manufacturing environment, such as jobs and resources in the context of industrial plant layout analysis. The study carried out enabled to obtain relevant results for the identification of relations among these entities for supporting to establish an appropriate plant layout for producing the jobs.

Abstract
Meta-Heuristics (MH) are the most used optimization techniques to approach Complex Combinatorial Problems (COPs). Their ability to move beyond the local optimums make them an especially attractive choice to solve complex computational problems, such as most scheduling problems. However, the knowledge of what Meta-Heuristics perform better in certain problems is based on experiments. Classic MH, as the Simulated Annealing (SA) has been deeply studied, but newer MH, as the Discrete Artificial Bee Colony (DABC) still need to be examined in more detail. In this paper DABC has been compared with SA in 30 academic benchmark instances of the weighted tardiness problem (1 parallel to Sigma w(j)T(j)). Both MH parameters were fine-tuned with Taguchi Experiments. In the computational study DABC performed better and the subsequent statistical study demonstrated that DABC is more prone to find near-optimum solutions. On the other hand SA appeared to be more efficient.

Abstract
Real world optimization problems like Scheduling are generally complex, large scaled, and constrained in nature. Thereby, classical operational research methods are often inadequate to efficiently solve them. Metaheuristics (MH) are used to obtain near-optimal solutions in an efficient way, but have different numerical and/or categorical parameters which make the tuning process a very time-consuming and tedious task. Learning methods can be used to aid with the parameter tuning process. Racing techniques have been used to evaluate, in a refined and efficient way, a set of candidates and discard those that appear to be less promising during the evaluation process. Case-based Reasoning (CBR) aims to solve new problems by using information about solutions to previous similar problems. A novel Racing+CBR approach is proposed and brings together the better of the two techniques. A computational study for the resolution of the scheduling problem is presented, concluding about the effectiveness of the proposed approach.