Fast continuous collision detection and handling for desktop virtual prototyping

This paper presents an overview of our recent work on continuous collision detection methods and constraints handling for rigid polyhedral objects. We demonstrate that continuous collision detection algorithms are practical in interactive dynamics simulation of complex polyhedral rigid bodies and show how continuous collision detection and efficient constraint-based dynamics algorithms allow us to perform various virtual prototyping tasks intuitively, precisely and robustly on commodity desktop computers. Especially, we present two applications of our system to actual industrial cases. We note that both tasks are performed with a simple 2D mouse on a high-end computer.     

FORMS: A flexible object recognition and modelling system

We describe a flexible object recognition and modelling system (FORMS) which represents and recognizes animate objects from their silhouettes. This consists of a model for generating the shapes of animate objects which gives a formalism for solving the inverse problem of object recognition. We model all objects at three levels of complexity: (i) the primitives, (ii) the mid-grained shapes, which are deformations of the primitives, and (iii) objects constructed by using a grammar to join mid-grained shapes together. The deformations of the primitives can be characterized by principal component analysis or modal analysis. When doing recognition the representations of these objects are obtained in a bottom-up manner from their silhouettes by a novel method for skeleton extraction and part segmentation based on deformable circles. These representations are then matched to a database of prototypical objects to obtain a set of candidate interpretations. These interpretations are verified in a top-down process. The system is demonstrated to be stable in the presence of noise, the absence of parts, the presence of additional parts, and considerable variations in articulation and viewpoint. Finally, we describe how such a representation scheme can be automatically learnt from examples.     

Transparent and Specular Object Reconstruction

This state of the art report covers reconstruction methods for transparent and specular objects or phenomena. While the 3D acquisition of opaque surfaces with Lambertian reflectance is a well‐studied problem, transparent, refractive, specular and potentially dynamic scenes pose challenging problems for acquisition systems. This report reviews and categorizes the literature in this field. Despite tremendous interest in object digitization, the acquisition of digital models of transparent or specular objects is far from being a solved problem. On the other hand, real‐world data is in high demand for applications such as object modelling, preservation of historic artefacts and as input to data‐driven modelling techniques. With this report we aim at providing a reference for and an introduction to the field of transparent and specular object reconstruction. We describe acquisition approaches for different classes of objects. Transparent objects/phenomena that do not change the straight ray geometry can be found foremost in natural phenomena. Refraction effects are usually small and can be considered negligible for these objects. Phenomena as diverse as fire, smoke, and interstellar nebulae can be modelled using a straight ray model of image formation. Refractive and specular surfaces on the other hand change the straight rays into usually piecewise linear ray paths, adding additional complexity to the reconstruction problem. Translucent objects exhibit significant sub‐surface scattering effects rendering traditional acquisition approaches unstable. Different classes of techniques have been developed to deal with these problems and good reconstruction results can be achieved with current state‐of‐the‐art techniques. However, the approaches are still specialized and targeted at very specific object classes. We classify the existing literature and hope to provide an entry point to this exiting field.     

Implementation of Web‐based argumentation in facilitating elementary school students to learn environmental issues

This research develops a Web‐based argumentation system named the Web‐based Interactive Argumentation System (WIAS). WIAS can provide teachers with the scaffolding for argumentation instruction. Students can propose their statements, collect supporting evidence and share and discuss with peers online. This research adopts a quasi‐experimental design, applying WIAS to the teaching of environmental issues, including mudslides, global warming and nuclear power. Fifty‐seven elementary school fifth graders from two classes participated in this research. With each class as a unit, they were divided into the WIAS group (n = 30) and the traditional argumentation instruction (TAI) group (n = 27). Before research, all students took the pre‐test of the ‘achievement test for environmental issues (ATEI)’ and the ‘environmental literacy scale (ELS).’ Then all students received argumentation training and six classes of argumentation instruction. Students in the WIAS group performed argumentation in the WIAS, while those in the TAI group performed argumentation in a traditional classroom. After the six‐class argumentation instruction, all students took the post‐test of the ATEI and ELS. The results show that students in the WIAS group have significantly better learning effectiveness than those in the TAI group. Students in the WIAS group also exhibited significantly better improvement in their environmental literacy.     

Modelling and learning user preferences over sets

Although there has been significant research on modelling and learning user preferences for various types of objects, there has been relatively little work on the problem of representing and learning preferences over sets of objects. We introduce a representation language, DD-PREF, that balances preferences for particular objects with preferences about the properties of the set. Specifically, we focus on the depth of objects (i.e. preferences for specific attribute values over others) and on the diversity of sets (i.e. preferences for broad vs. narrow distributions of attribute values). The DD-PREF framework is general and can incorporate additional object- and set-based preferences. We describe a greedy algorithm, DD-Select, for selecting satisfying sets from a collection of new objects, given a preference in this language. We show how preferences represented in DD-PREF can be learned from training data. Experimental results are given for three domains: a blocks world domain with several different task-based preferences, a real-world music playlist collection, and rover image data gathered in desert training exercises.     

Reusing knowledge in embedded systems modelling

Model‐based design is a promising technique to improve the quality of software and the efficiency of the software development process. We are investigating how to efficiently model embedded software and its environment to verify the requirements for the system controlled by the software. The software environment consists of mechanical, electrical and other parts; modelling it involves learning how these parts work, deciding what is relevant to model and how to model it. It is not possible to fully automate these steps. There are general guidelines, but given that every modelling problem differs, much is left to the modeller's own preference, background and experience. Still, when the next generation of a system is designed, the new system will have common elements with its previous version. Therefore, lessons learned from the current model could inform future models. We propose a framework for identifying the non‐formal elements of knowledge, insights and a model itself, which can support modelling of the next system generation. We will present the application of our framework on an action research case – modelling mechanical parts of a paper‐inserting machine.     

Object-oriented Analysis with Structured and Integrated Specifications and Solutions (OASISS) for production system control

Tool specification for production system control requires the identification of the main characteristics of the production system. The production systems are varied, and the production control tools (such as scheduling algorithms and resource assignment rules) are often dedicated to only one class of these systems. Tool assessment for production system control requires modelling of the production system. We propose a new approach to production systems, to identify the nature and the complexity of the system according to the classification of Conway et al., and to specify a dynamic model that will allow us to simulate production control decisions. This paper presents this object-oriented approach, which identifies the resources and the organization features of the studied system. It then, proposes some predetermined classes of objects, which enable us to classify the system components and to use pre-existing dynamic models. These dynamic models are based on timed and coloured Petri nets. The overall architecture of our methodology follows three steps: top-down analysis of resources and bottom-up recognition of predetermined objects, instantiation and integration of objects, and implementation of objects. An example illustrates its use in the textile industry.     

Canonical processes in active reading and hypervideo production

Active reading of audiovisual documents is an iterative activity, dedicated to the analysis of the audiovisual source through its enrichment with structured metadata and the definition of appropriate visualisation means for this metadata, producing new multimedia objects called hypervideos. We will describe in this article the general decomposition of active reading and how it is put into practice in the Advene framework, analysing how its activities fit into the Canonical Media Processes model.     

Net.Create: Network Visualization to Support Collaborative Historical Knowledge Building

Students across disciplines struggle with sensemaking when they are faced with the need to understand and analyze massive amounts of information. This is particularly salient in the disciplines of both history and data science. Our approach to helping students build expertise with complex information leverages activity theory to think about the design of a classroom activity system integrated with the design of a collaborative open-source network-analysis software tool called Net.Create. Through analysis of network log data as well as video data of students’ collaborative interactions with Net.Create, we explore how our activity system helped students reconcile common contradictions that create barriers to dealing with complex datasets in large lecture classrooms. Findings show that as students draw on details in a historical text to collaboratively construct a larger network, they begin to move more readily between small detail and aggregate overview. Students at both high and low initial skill levels were able to increase the complexity of their historical analyses through their engagement with the Net.Create tool and activities. Net.Create transforms the limitation of large class sizes in history classrooms into a resource for students’ collaborative knowledge building, and through collaborative data entry it supports the historiographic practices of citation and revision and helps students embed local historical actors into a larger historical context.

     

Laying out objects with geometric and physical constraints

Modeling scenes involves two tasks:object modeling andobject layout. This paper focuses on object layout and proposes a constraint-based approach which yields a powerful object layout environment. The approach uses collision detection and physical simulation to ensure geometric and physical consistency of the resulting scenes, such as no interpenetration, and physical stability of the objects. A prototype system is developed, providing six basic operations; PUT, PUSH/PULL, TURN/TILT, PICK-UP, TRANSLATE, and ROTATE. The system: ensures geometric and physical consistency; provides easy-to-use operations analogous to object placement in real life; allows twodimensional control easily specified by mouse. Interactive speed is achieved on graphics workstations by using rasterized collision detection and simple quasi-static motion simulation. The system is interfaced to modeling/rendering/animation systems, and realizes an integrated environment for object modeling, object layout, rendering, and animation. We describe several scenes that have been modeled using the system and argue that these experiments confirm that the scene modeling task is greatly simplified by our constraint-based approach.     

From deconstructive misalignment to constructive alignment: Exploring student uses of mobile technologies in university classrooms

Becoming increasingly ubiquitous for students are the various uses of information and communication technologies (ICTs) within their wireless and networked learning environments. Many students use ICTs during lectures or tutorials for tasks unrelated to class learning activities, thus providing a potential misalignment with the intended learning outcomes. A sample of undergraduate and postgraduate psychology and business students were surveyed to determine their frequency of mobile ICT use in the classroom and the students' motivations and rationale for undertaking those activities unrelated to classroom learning. The survey contained quantitative items (categorical and Likert scale response items) and qualitative items requiring responses to open ended questions. Students indicated that using ICTs in ways misaligned with lesson learning outcomes, was related to the course content delivery mode, the promotion of passive or active learning, being domestic or international students, learner maturity, and the need for supporting course information. Understanding how and why students use mobile ICTs in classes can inform the redesign of classroom activities, to actively involve those technologies to assist in constructive alignment with the learning outcomes and enhance the student learning experience.     

Classifying dynamic objects

For robots operating in real-world environments, the ability to deal with dynamic entities such as humans, animals, vehicles, or other robots is of fundamental importance. The variability of dynamic objects, however, is large in general, which makes it hard to manually design suitable models for their appearance and dynamics. In this paper, we present an unsupervised learning approach to this model-building problem. We describe an exemplar-based model for representing the time-varying appearance of objects in planar laser scans as well as a clustering procedure that builds a set of object classes from given observation sequences. Extensive experiments in real environments demonstrate that our system is able to autonomously learn useful models for, e.g., pedestrians, skaters, or cyclists without being provided with external class information.     

Integration testing of object‐oriented components using finite state machines

In object‐oriented terms, one of the goals of integration testing is to ensure that messages from objects in one class or component are sent and received in the proper order and have the intended effect on the state of the objects that receive the messages. This research extends an existing single‐class testing technique to integration testing of multiple classes. The single‐class technique models the behaviour of a single class as a finite state machine, transforms the representation into a data flow graph that explicitly identifies the definitions and uses of each state variable of the class, and then applies conventional data flow testing to produce test case specifications that can be used to test the class. This paper extends those ideas to inter‐class testing by developing flow graphs, finding paths between pairs of definitions and uses, detecting some infeasible paths and automatically generating tests for an arbitrary number of classes and components. It introduces flexible representations for message sending and receiving among objects and allows concurrency among any or all classes and components. Data flow graphs are stored in a relational database and database queries are used to gather def‐use information. This approach is conceptually simple, mathematically precise, quite powerful and general enough to be used for traditional data flow analysis. This testing approach relies on finite state machines, database modelling and processing techniques and algorithms for analysis and traversal of directed graphs. The paper presents empirical results of the approach applied to an automotive system. This work was prepared by U.S. Government employees as part of their official duties and is, therefore, a work of the U.S. Government and not subject to copyright. Published in 2006 by John Wiley & Sons, Ltd.     

Pattern recognition by means of disjoint principal components models

Pattern recognition based on modelling each separate class by a separate principal components (PC) model is discussed. These PC models are shown to be able to approximate any continuous variation within a single class. Hence, methods based on PC models will, provided that the data are sufficient, recognize any pattern that exists in a given set of objects. In addition, fitting the objects in each class by a separate PC model will, in a simple way, provide information about such matters as the relevance of single variables, “outliers” among the objects and “distances” between different classes. Application to the classical Iris-data of Fisher is used as an illustration.     

Ontological and linguistic metamodelling revisited: A language use approach

ContextAlthough metamodelling is generally accepted as important for our understanding of software and systems development, arguments about the validity and utility of ontological versus linguistic metamodelling continue.ObjectiveThe paper examines the traditional, metamodel-focused construction of modelling languages in the context of language use, and particularly speech act theory. These concepts are then applied to the problems introduced by the “Orthogonal Classification Architecture” that is often called the ontological/linguistic paradox. The aim of the paper is to show how it is possible to overcome these problems.MethodThe paper adopts a conceptual–analytical approach by revisiting the published arguments and developing an alternative metamodelling architecture based on language use.ResultsThe analysis shows that when we apply a language use perspective of meaning to traditional modelling concepts, a number of incongruities and misconceptions in the traditional approaches are revealed – issues that are not evident in previous work based primarily on set theory. Clearly differentiating between the extensional and intensional aspects of class concepts (as sets) and also between objects (in the social world) and things (in the physical world) allows for a deeper understanding to be gained of the relationship between the ontological and linguistic views promulgated in the modelling world.ConclusionsWe propose that a viewpoint that integrates language use ideas into traditional modelling (and metamodelling) is vital, and stress that meaning is not inherent in the physical world; meaning, and thus socially valid objects, are constructed by use of language, which may or may not establish a one-to-one correspondence relationship between objects and physical things.