Figure 1: We develop GRASS, a Generative Recursive Autoencoder for Shape Structures, which enables structural blending between two 3D shapes. Note the discrete blending of translational symmetries (slats on the chair backs) and rotational symmetries (the swivel legs). GRASS encodes and synthesizes box structures (bottom) and part geometries (top) separately. The blending is performed on fixed-length codes learned by the unsupervised autoencoder, without any form of part correspondences, given or computed. AbstractWe introduce a novel neural network architecture for encoding and synthesis of 3D shapes, particularly their structures. Our key insight is that 3D shapes are effectively characterized by their hierarchical organization of parts, which reflects fundamental intra-shape relationships such as adjacency and symmetry. We develop a recursive neural net (RvNN) based autoencoder to map a flat, unlabeled, arbitrary part layout to a compact code. The code effectively captures hierarchical structures of man-made 3D objects of varying structural complexities despite being fixed-dimensional: an associated decoder maps a code back to a full hierarchy. The learned bidirectional mapping is further tuned using an adversarial setup to yield a generative model of plausible structures, from which novel structures can be sampled. Finally, our structure synthesis framework is augmented by a second trained module that produces fine-grained part geometry, conditioned on global and local structural context, leading to a full generative pipeline for 3D shapes. We demonstrate that without supervision, our network learns meaningful structural hierarchies adhering to perceptual grouping principles, produces compact codes which enable applications such as shape classification and partial matching, and supports shape synthesis and interpolation with significant variations in topology and geometry.
The main objective of this paper is to analyze the representativeness of the SPEM (Software Process Engineering Metamodel Specification) and the BPMN (Business Process Modeling Notation) standards in the software processes modeling context. To perform this analysis, it was adopted a standard structure to define a software process based upon a process ontology. Then, the SPEM and BPMN standards notations and their semantically corresponding elements in the default process were identified. This mapping also includes components of the CMMI-DEV (Capability Maturity Model Integration for Development) and MR-MPS (Reference Model for Software Process Improvement) quality models. This was necessary to assist in the mapping evaluation through a case study which models the best practices of these quality models. Finally, we carried out an analysis of these standards through specific characteristics considered necessary to model and to represent software processes.
The working environment of railways is challenging and complex and often involves high-risk operations. These operations affect both the company staff and inhabitants of the towns and cities alongside the railway lines. To reduce the employees' and public's exposure to risk, railway companies adopt strategies involving trained safety personnel, advanced forms of technology, and special work processes. Nevertheless, unfortunate incidents still occur. To assist railway safety management, researchers developed a visual-analytics system. Using a data analytics workflow, it compiles an incident risk index that processes information about railway incidents. It displays the index on a geographical map, together with socioeconomic information about the associated towns and cities. Feedback on this system suggests that safety engineers and experts can use it to make and communicate decisions.
We present a fully automatic method that finds a small number of machine fabricable wires with minimal overlap to reproduce a wire sculpture design as a 3D shape abstraction. Importantly, we consider non-planar wires, which can be fabricated by a wire bending machine, to enable efficient construction of complex 3D sculptures that cannot be achieved by previous works. We call our wires Eulerian wires , since they are as Eulerian as possible with small overlap to form the target design together. Finding such Eulerian wires is highly challenging, due to an enormous search space. After exploring a variety of optimization strategies, we formulate a population-based hybrid metaheuristic model, and design the join, bridge and split operators to refine the solution wire sets in the population. We start the exploration of each solution wire set in a bottom-up manner, and adopt an adaptive simulated annealing model to regulate the exploration. By further formulating a meta model on top to optimize the cooling schedule, and precomputing fabricable subwires, our method can efficiently find promising solutions with low wire count and overlap in one to two minutes. We demonstrate the efficiency of our method on a rich variety of wire sculptures, and physically fabricate several of them. Our results show clear improvements over other optimization alternatives in terms of solution quality, versatility, and scalability.
SPEM (Software Process Engineering Metamodel Specification) is the software processes modeling standard defined by OMG (Object Management Group). However, the process enactment support provided by this standard has many deficiencies. Therefore, the main objective of this paper is to propose a language for software process enactment based upon SPEM 2.0 concepts. First, we will present a critical analysis of the SPEM standard approach for enactment. Then, we will present xSPIDER_ML, an enactment language, and describe its structure, components and associated rules. In order to evaluate the proposed language, a case study is performed through a RUP (Rational Unified Process) process instantiation. The language presented in this paper is part of a support set of tools for flexible software process enactment. Additionally, this set of tools is in compliance with software process quality models.
This paper presents a case study aiming to investigate which variant of the Think-Aloud Protocol (i.e., the Concurrent Think-Aloud and the Retrospective Think-Aloud) better integrates with the Cognitive Walkthrough with Users. To this end we performed a case study that involved twelve users and one usability evaluator. Usability problems uncovered by each method were evaluated to help us understand the strengths and weaknesses of the studied usability testing methods. The results suggest that 1) the Cognitive Walkthrough with Users integrates equally well with both the Think-Aloud Protocol variants; 2) the Retrospective Think-Aloud find more usability problems and 3) the Concurrent Think-Aloud is slightly faster to perform and was more cost effective. However, this is only one case study, and further research is needed to verify if the results are actually statistically significant.
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