Knowledge Building of 3D Geometry Concepts and Processes within a Virtual Reality Learning Environment

This paper reports on a pilot study for a prototype VRLE (Virtual Reality Learning Environment) named VRMath. The two primary school students who were involved in this study engaged in two VRMath learning activities designed by the researchers. The results indicated that 3D navigation within the VR 3D space was difficult. However, it could be aided with the navigation aids designed within VRMath. The 3D navigation within the 3D virtual space also caused the participants confusion in terms of their spatial visualisation and orientation abilities. The construction of 3D geometrical objects within VRMath was also difficult especially when the participants were operating the 3D rotation mentally and physically with respect to their body (i.e., the egocentric frame of reference). It was found that the simultaneously use of different frames of reference could help the construction of 3D geometrical objects. During the learning activities, issues about the usability of VRMath were also explored

Parallel software tools at Langley Research Center

This document gives a brief overview of parallel software tools available on the Intel iPSC/860 parallel computer at Langley Research Center. It is intended to provide a source of information that is somewhat more concise than vendor-supplied material on the purpose and use of various tools. Each of the chapters on tools is organized in a similar manner covering an overview of the functionality, access information, how to effectively use the tool, observations about the tool and how it compares to similar software, known problems or shortfalls with the software, and reference documentation. It is primarily intended for users of the iPSC/860 at Langley Research Center and is appropriate for both the experienced and novice user

Explainable and Advisable Learning for Self-driving Vehicles

Deep neural perception and control networks are likely to be a key component of self-driving vehicles. These models need to be explainable - they should provide easy-to-interpret rationales for their behavior - so that passengers, insurance companies, law enforcement, developers, etc., can understand what triggered a particular behavior. Explanations may be triggered by the neural controller, namely introspective explanations, or informed by the neural controller's output, namely rationalizations. Our work has focused on the challenge of generating introspective explanations of deep models for self-driving vehicles. In Chapter 3, we begin by exploring the use of visual explanations. These explanations take the form of real-time highlighted regions of an image that causally influence the network's output (steering control). In the first stage, we use a visual attention model to train a convolution network end-to-end from images to steering angle. The attention model highlights image regions that potentially influence the network's output. Some of these are true influences, but some are spurious. We then apply a causal filtering step to determine which input regions actually influence the output. This produces more succinct visual explanations and more accurately exposes the network's behavior. In Chapter 4, we add an attention-based video-to-text model to produce textual explanations of model actions, e.g. "the car slows down because the road is wet". The attention maps of controller and explanation model are aligned so that explanations are grounded in the parts of the scene that mattered to the controller. We explore two approaches to attention alignment, strong- and weak-alignment. These explainable systems represent an externalization of tacit knowledge. The network's opaque reasoning is simplified to a situation-specific dependence on a visible object in the image. This makes them brittle and potentially unsafe in situations that do not match training data. In Chapter 5, we propose to address this issue by augmenting training data with natural language advice from a human. Advice includes guidance about what to do and where to attend. We present the first step toward advice-giving, where we train an end-to-end vehicle controller that accepts advice. The controller adapts the way it attends to the scene (visual attention) and the control (steering and speed). Further, in Chapter 6, we propose a new approach that learns vehicle control with the help of long-term (global) human advice. Specifically, our system learns to summarize its visual observations in natural language, predict an appropriate action response (e.g. "I see a pedestrian crossing, so I stop"), and predict the controls, accordingly

Knowledge Construction of 3D Geometry Concepts and Processes Within a Virtual Reality Learning Environment

A consensus has emerged within the mathematics education community about the limitations of traditional approaches for teaching and learning 3D geometry. Therefore, it has been suggested that new approaches based on the use of computers need to be adopted. One such new approach that has been proposed utilises Virtual Reality Learning Environment (VRLE). This paper reports on the initial phases of a research study whose major aim is to design and evaluate a VRLE to facilitate the construction of knowledge about 3D geometry concepts and processes. This research study investigates two primary school students’ construction of 3D geometry knowledge whilst engaged within a VRLE developed by the researcher. A design experiments research methodology was employed in this study. This is research that iterates through cycles of design and research with the objective of arriving at theoretical and design principles that will have application both within and beyond the immediate research study. Therefore, the results being reported in this paper will be used to inform the modification not only of the VRLE but also of theoretical frameworks underlying the design and implementation of VRLEs

On Verifying Complex Properties using Symbolic Shape Analysis

One of the main challenges in the verification of software systems is the analysis of unbounded data structures with dynamic memory allocation, such as linked data structures and arrays. We describe Bohne, a new analysis for verifying data structures. Bohne verifies data structure operations and shows that 1) the operations preserve data structure invariants and 2) the operations satisfy their specifications expressed in terms of changes to the set of objects stored in the data structure. During the analysis, Bohne infers loop invariants in the form of disjunctions of universally quantified Boolean combinations of formulas. To synthesize loop invariants of this form, Bohne uses a combination of decision procedures for Monadic Second-Order Logic over trees, SMT-LIB decision procedures (currently CVC Lite), and an automated reasoner within the Isabelle interactive theorem prover. This architecture shows that synthesized loop invariants can serve as a useful communication mechanism between different decision procedures. Using Bohne, we have verified operations on data structures such as linked lists with iterators and back pointers, trees with and without parent pointers, two-level skip lists, array data structures, and sorted lists. We have deployed Bohne in the Hob and Jahob data structure analysis systems, enabling us to combine Bohne with analyses of data structure clients and apply it in the context of larger programs. This report describes the Bohne algorithm as well as techniques that Bohne uses to reduce the ammount of annotations and the running time of the analysis

Collaborative research on V/STOL control system/cockpit display tradeoffs under the NASA/MOD joint aeronautical program

Summarized here are activities that have taken place from 1979 to the present in a collaborative program between NASA Ames Research Center and the Royal Aerospace Establishment (now Defence Research Agency), Bedford on flight control system and cockpit display tradeoffs for low-speed and hover operations of future V/STOL aircraft. This program was created as Task 8A of the Joint Aeronautical Program between NASA in the United States and the Ministry of Defence (Procurement Executive) in the United Kingdom. The program was initiated based on a recognition by both parties of the strengths of the efforts of their counterparts and a desire to participate jointly in future simulation and flight experiments. In the ensuing years, teams of NASA and RAE engineers and pilots have participated in each other's simulation experiments to evaluate control and display concepts and define design requirements for research aircraft. Both organizations possess Harrier airframes that have undergone extensive modification to provide in-flight research capabilities in the subject areas. Both NASA and RAE have profited by exchanges of control/display concepts, design criteria, fabrication techniques, software development and validation, installation details, and ground and flight clearance techniques for their respective aircraft. This collaboration has permitted the two organizations to achieve jointly substantially more during the period than if they had worked independently. The two organizations are now entering the phase of flight research for the collaborative program as currently defined

Experimental results with a six-degree-of-freedom force-reflecting hand controller

Control experiments performed using an isotonic joystick connected to a six degree-of-freedom manipulator equipped with a six dimensional force-torque sensor at the base of the manipulator end effector are described. The preliminary control experiments were aimed at the investigation of the human operators' ability to command and control forces in different directions by varying the information conditions and the values of the feedforward and feedback command gains in the bilateral control loop. The main conclusions are: (1) a quantified graphic display of force-torque information can considerably enhance the operator's ability to perform a quantitatively sharp force-torque control, and (2) there seems to be a task dependent optimal combination of the feedforward and feedback command gain values which provide a dynamically smooth and stable bilateral control performance