The Effects of Task, Task Mapping, and Layout Space on User Performance in Information-Rich Virtual Environments

How should abstract information be displayed in Information-Rich Virtual Environments (IRVEs)? There are a variety of techniques available, and it is important to determine which techniques help foster a user’s understanding both within and between abstract and spatial information types. Our evaluation compared two such techniques: Object Space and Display Space. Users strongly prefer Display Space over Object Space, and those who use Display Space may perform better. Display Space was faster and more accurate than Object Space for tasks comparing abstract information. Object Space was more accurate for comparisons of spatial information. These results suggest that for abstract criteria, visibility is a more important requirement than perceptual coupling by depth and association cues. They also support the value of perceptual coupling for tasks with spatial criteria

Self-directedness, integration and higher cognition

In this paper I discuss connections between self-directedness, integration and higher cognition. I present a model of self-directedness as a basis for approaching higher cognition from a situated cognition perspective. According to this model increases in sensorimotor complexity create pressure for integrative higher order control and learning processes for acquiring information about the context in which action occurs. This generates complex articulated abstractive information processing, which forms the major basis for higher cognition. I present evidence that indicates that the same integrative characteristics found in lower cognitive process such as motor adaptation are present in a range of higher cognitive process, including conceptual learning. This account helps explain situated cognition phenomena in humans because the integrative processes by which the brain adapts to control interaction are relatively agnostic concerning the source of the structure participating in the process. Thus, from the perspective of the motor control system using a tool is not fundamentally different to simply controlling an arm

Exploiting 2D Floorplan for Building-scale Panorama RGBD Alignment

This paper presents a novel algorithm that utilizes a 2D floorplan to align panorama RGBD scans. While effective panorama RGBD alignment techniques exist, such a system requires extremely dense RGBD image sampling. Our approach can significantly reduce the number of necessary scans with the aid of a floorplan image. We formulate a novel Markov Random Field inference problem as a scan placement over the floorplan, as opposed to the conventional scan-to-scan alignment. The technical contributions lie in multi-modal image correspondence cues (between scans and schematic floorplan) as well as a novel coverage potential avoiding an inherent stacking bias. The proposed approach has been evaluated on five challenging large indoor spaces. To the best of our knowledge, we present the first effective system that utilizes a 2D floorplan image for building-scale 3D pointcloud alignment. The source code and the data will be shared with the community to further enhance indoor mapping research

Recurrent Neural Networks For Accurate RSSI Indoor Localization

This paper proposes recurrent neuron networks (RNNs) for a fingerprinting indoor localization using WiFi. Instead of locating user's position one at a time as in the cases of conventional algorithms, our RNN solution aims at trajectory positioning and takes into account the relation among the received signal strength indicator (RSSI) measurements in a trajectory. Furthermore, a weighted average filter is proposed for both input RSSI data and sequential output locations to enhance the accuracy among the temporal fluctuations of RSSI. The results using different types of RNN including vanilla RNN, long short-term memory (LSTM), gated recurrent unit (GRU) and bidirectional LSTM (BiLSTM) are presented. On-site experiments demonstrate that the proposed structure achieves an average localization error of $0.75$ m with $80\%$ of the errors under $1$ m, which outperforms the conventional KNN algorithms and probabilistic algorithms by approximately $30\%$ under the same test environment.Comment: Received signal strength indicator (RSSI), WiFi indoor localization, recurrent neuron network (RNN), long shortterm memory (LSTM), fingerprint-based localizatio

Developing an understanding of the nature of accessibility and usability problems blind students face in web-enhanced instruction environments

The central premise of this research is that blind and visually impaired (BVI) people cannot use the Internet effectively due to accessibility and usability problems. Use of the Internet is indispensable in today's education system that relies on Web-enhanced instruction (WEI). Therefore, BVI students cannot participate effectively in WEI. Extant literature recognizes that non-visual Web interaction is inherently challenging. However, it does not explain where, how and why BVI students face accessibility and usability problems in performing academic tasks in WEI. This knowledge is necessary to adequately inform the development of interventions that improve the functional and academic outcomes of BVI students in WEI. The purpose of this doctoral research is to understand the nature of accessibility and usability problems BVI students face in WEI environments. It adopts a novel user-centered, task-oriented, cognitive approach to develop an in-depth, contextually-situated, observational and experiential knowledge of these problems. The context of WEI experience under investigation is an online exam over a typical course management system. Research design is a qualitative field study that involves a multimethod evaluation of the WEI environment. The core component of this multimethod evaluation is BVI students' assessment of the WEI environment. This is triangulated through assessments made by WCAG (Web Content Accessibility Guidelines) and Web developers. The BVI student assessment employs an integrated problem solving model, in combination with verbal protocol analysis, to identify and understand where, how and why BVI students face a problem in completing the exam. The WCAG assessment employs automated accessibility testing and WCAG textual analysis to identify interface objects that violate accessibility standards and characterizes a problem. The Web developer assessment involves open-ended interviews to identify the source of a problem. Results show that the WEI environment consisted of innumerable interface objects that violated WCAG's standards on Web accessibility and usability. BVI participants faced many accessibility and usability problems that posed significant challenges completing the online exam. These problems fall into six major problem types as described below: 1. Confusion while navigating across WEI environment due to its frame-based page structure without unique frame names; 2. Susceptibility to submitting incomplete work when a new question page does not provide location and contextual information; 3. Difficulty understanding how to submit work when the selection controls for multiple option questions lack a consistent keyboard navigation procedure; 4. Inability to negotiate security information pop-up when the WEI environment uses an alert dialogue box; 5. Ambiguity in essay-type question page that lack meaningful labels for interface objects, including text area and text formatting toolbar; 6. Vulnerability of losing work when Backspace behaves as browser's Back button inside text area. This doctoral research contributes in three ways. It fills the knowledge gap about the nature of problems BVI students face in Web interactions for academic tasks. This kind of knowledge is necessary to determine accessibility and usability requirements for WEI. Another contribution is a set of mental model representations that explicate the thought processes of BVI students. Such representations are useful in developing user instruction and design of more accessible and usable Web sites. A third contribution is a user-centered, task-based, cognitive and multi-method approach to evaluate Web accessibility and usability