Nomadic Speech-Based Text Entry: A Decision Model Strategy for Improved Speech to Text Processing

Speech text entry can be problematic during ideal dictation conditions, but difficulties are magnified when external conditions deteriorate. Motion during speech is an extraordinary condition that might have detrimental effects on automatic speech recognition. This research examined speech text entry while mobile. Speech enrollment profiles were created by participants in both a seated and walking environment. Dictation tasks were also completed in both the seated and walking conditions. Although results from an earlier study suggested that completing the enrollment process under more challenging conditions may lead to improved recognition accuracy under both challenging and less challenging conditions, the current study provided contradictory results. A detailed review of error rates confirmed that some participants minimized errors by enrolling under more challenging conditions while others benefited by enrolling under less challenging conditions. Still others minimized errors when different enrollment models were used under the opposing condition. Leveraging these insights, we developed a decision model to minimize recognition error rates regardless of the conditions experienced while completing dictation tasks. When applying the model to existing data, error rates were reduced significantly but additional research is necessary to effectively validate the proposed solution.     

An Investigation of Information Systems Interoperability in UK Universities: Findings and Recommendations

This article reports on the key findings and implications of the Joint Information Systems Committee (JISC)-funded Online Catalogue and Repository Interoperability Study (OCRIS), a 3-month project which investigated the interoperability of Online Public Access Catalogues (OPACs) and Institutional Repositories (IRs) within UK universities. A series of detailed recommendations enumerate some of the ways in which they might begin to develop and support an interoperable systems landscape to the benefit of all key stakeholders.

The project combined quantitative and qualitative research methods including an online questionnaire distributed to staff within 85 universities, desk research, and two case studies conducted at the Universities of Cambridge and Glasgow.     

Casting shadows in the science classroom

Abstract

Research in mathematics and science education frequently directs one's attention to the limited content knowledge of elementary pre‐service teachers. It is believed, however, that research of this nature leads to a deficit approach to understanding more about the teaching and learning of these subjects. In addition to focusing on the knowers of school mathematics and science, there is a call to acknowledge the problematic nature of knowing (in) mathematics and science. In this paper, the metaphor of shadows is used in a critical exploration of what it means to know and how the cultures of classrooms have shaped these images of knowing. Through the voices of pre‐service teachers, this paper directs one's attention to objects that cast shadows on the learning and knowing of mathematics and science. Three such shadow‐casting objects discussed in this paper are textbooks, teachers, and gendered ideology. The paper seeks to critically illuminate the problematic nature of what it means to know (in) mathematics and science by examining the shadowy texture of unquestioned ideologies.     

Online collaboration: Collaborative behavior patterns and factors affecting globally distributed team performance

Studying the collaborative behavior of online learning teams and how this behavior is related to communication mode and task type is a complex process. Research about small group learning suggests that a higher percentage of social interactions occur in synchronous rather than asynchronous mode, and that students spend more time in task-oriented interaction in asynchronous discussions than in synchronous mode. This study analyzed the collaborative interaction patterns of global software development learning teams composed of students from Turkey, US, and Panama. Data collected from students’ chat histories and forum discussions from three global software development projects were collected and compared. Both qualitative and quantitative analysis methods were used to determine the differences between a group’s communication patterns in asynchronous versus synchronous communication mode. K-means clustering with the Ward method was used to investigate the patterns of behaviors in distributed teams. The results show that communication patterns are related to communication mode, the nature of the task, and the experience level of the leader. The paper also includes recommendations for building effective online collaborative teams and describes future research possibilities.     

Parallel force measurement with a polymeric microbeam array using an optical microscope and micromanipulator

An image analysis method and its validation are presented for tracking the displacements of parallel mechanical force sensors. Force is measured using a combination of beam theory, optical microscopy, and image analysis. The primary instrument is a calibrated polymeric microbeam array mounted on a micromanipulator with the intended purpose of measuring traction forces on cell cultures or cell arrays. One application is the testing of hypotheses involving cellular mechanotransduction mechanisms. An Otsu-based image analysis code calculates displacement and force on cellular or other soft structures by using edge detection and image subtraction on digitally captured optical microscopy images. Forces as small as 250+/-50 nN and as great as 25+/-2.5 microN may be applied and measured upon as few as one or as many as hundreds of structures in parallel. A validation of the method is provided by comparing results from a rigid glass surface and a compliant polymeric surface.     

Online Social Network Adoption: A Cross-Cultural Study

This research explores the extent to which users across cultures adopt the technology of online social networks (OSNs) in order to promote or support a social cause. By surveying graduate-level university students at institutions in the United States, China and India, this research builds on prior work in technology acceptance to model and explain how three elements of the task domain – the cultural aspects of the user, the social nature of the technology, and the social nature of the task – combine to influence the constructs and relationships within a modified Technology Acceptance Model (TAM) framework as well as the behavior that flows from it. This study contributes to our understanding of technology adoption by showing how OSNs are adopted by users across cultures in promoting and supporting social causes.     

Characterizing 3D vegetation structure from space: Mission requirements

Human and natural forces are rapidly modifying the global distribution and structure of terrestrial ecosystems on which all of life depends, altering the global carbon cycle, affecting our climate now and for the foreseeable future, causing steep reductions in species diversity, and endangering Earth's sustainability.To understand changes and trends in terrestrial ecosystems and their functioning as carbon sources and sinks, and to characterize the impact of their changes on climate, habitat and biodiversity, new space assets are urgently needed to produce high spatial resolution global maps of the three-dimensional (3D) structure of vegetation, its biomass above ground, the carbon stored within and the implications for atmospheric green house gas concentrations and climate. These needs were articulated in a 2007 National Research Council (NRC) report (NRC, 2007) recommending a new satellite mission, DESDynI, carrying an L-band Polarized Synthetic Aperture Radar (Pol-SAR) and a multi-beam lidar (Light RAnging And Detection) operating at 1064 nm. The objectives of this paper are to articulate the importance of these new, multi-year, 3D vegetation structure and biomass measurements, to briefly review the feasibility of radar and lidar remote sensing technology to meet these requirements, to define the data products and measurement requirements, and to consider implications of mission durations. The paper addresses these objectives by synthesizing research results and other input from a broad community of terrestrial ecology, carbon cycle, and remote sensing scientists and working groups. We conclude that:

(1)

Current global biomass and 3-D vegetation structure information is unsuitable for both science and management and policy. The only existing global datasets of biomass are approximations based on combining land cover type and representative carbon values, instead of measurements of actual biomass. Current measurement attempts based on radar and multispectral data have low explanatory power outside low biomass areas. There is no current capability for repeatable disturbance and regrowth estimates.

(2)

The science and policy needs for information on vegetation 3D structure can be successfully addressed by a mission capable of producing (i) a first global inventory of forest biomass with a spatial resolution 1 km or finer and unprecedented accuracy (ii) annual global disturbance maps at a spatial resolution of 1 ha with subsequent biomass accumulation rates at resolutions of 1 km or finer, and (iii) transects of vertical and horizontal forest structure with 30 m along-transect measurements globally at 25 m spatial resolution, essential for habitat characterization.

We also show from the literature that lidar profile samples together with wall-to-wall L-band quad-pol-SAR imagery and ecosystem dynamics models can work together to satisfy these vegetation 3D structure and biomass measurement requirements. Finally we argue that the technology readiness levels of combined pol-SAR and lidar instruments are adequate for space flight. Remaining to be worked out, are the particulars of a lidar/pol-SAR mission design that is feasible and at a minimum satisfies the information and measurement requirement articulated herein.     

Effects of imperfect automation on decision making in a simulated command and control task

OBJECTIVE: Effects of four types of automation support and two levels of automation reliability were examined. The objective was to examine the differential impact of information and decision automation and to investigate the costs of automation unreliability. BACKGROUND: Research has shown that imperfect automation can lead to differential effects of stages and levels of automation on human performance. Method: Eighteen participants performed a "sensor to shooter" targeting simulation of command and control. Dependent variables included accuracy and response time of target engagement decisions, secondary task performance, and subjective ratings of mental work-load, trust, and self-confidence. RESULTS: Compared with manual performance, reliable automation significantly reduced decision times. Unreliable automation led to greater cost in decision-making accuracy under the higher automation reliability condition for three different forms of decision automation relative to information automation. At low automation reliability, however, there was a cost in performance for both information and decision automation. CONCLUSION: The results are consistent with a model of human-automation interaction that requires evaluation of the different stages of information processing to which automation support can be applied. APPLICATION: If fully reliable decision automation cannot be guaranteed, designers should provide users with information automation support or other tools that allow for inspection and analysis of raw data.     

A practical approximation algorithm for the LMS line estimator

The problem of fitting a straight line to a finite collection of points in the plane is an important problem in statistical estimation. Robust estimators are widely used because of their lack of sensitivity to outlying data points. The least median-of-squares (LMS) regression line estimator is among the best known robust estimators. Given a set of n points in the plane, it is defined to be the line that minimizes the median squared residual or, more generally, the line that minimizes the residual of any given quantile q, where 0<q?1. This problem is equivalent to finding the strip defined by two parallel lines of minimum vertical separation that encloses at least half of the points.The best known exact algorithm for this problem runs in O(n²) time. We consider two types of approximations, a residual approximation, which approximates the vertical height of the strip to within a given error bound ε_r?0, and a quantile approximation, which approximates the fraction of points that lie within the strip to within a given error bound ε_q?0. We present two randomized approximation algorithms for the LMS line estimator. The first is a conceptually simple quantile approximation algorithm, which given fixed q and ε_q>0 runs in O(nlogn) time. The second is a practical algorithm, which can solve both types of approximation problems or be used as an exact algorithm. We prove that when used as a quantile approximation, this algorithm's expected running time is . We present empirical evidence that the latter algorithm is quite efficient for a wide variety of input distributions, even when used as an exact algorithm.     

Precision of the CAESAR scan-extracted measurements

Three-dimensional (3D) body scanners are increasingly used to derive 1D body dimensions from 3D whole body scans for instance, as input for clothing grading systems to make made-to-measure clothing or for width and depth dimensions of a seated workstation. In this study, the precision of the scanner-derived 1D dimensions from the CAESAR survey, a multinational anthropometric survey, was investigated. Two combinations of scanning teams with 3D whole body scanners were compared, one called the US Team and the other the Dutch Team. Twenty subjects were measured three times by one scanner and one team, and three times by the other combination. The subjects were marked prior to scanning using small dots, and the linear distances between the dots were calculated after processing the scans. The mean absolute difference (MAD) of the repetitions was calculated and this was compared to reported acceptable errors in manual measurements from the US Army's ANSUR survey when similar measurements were available. In addition, the coefficient of variation (CV) was calculated for all measurements. The results indicate that the CAESAR scan-extracted measurements are highly reproducible; for most measures the MAD is less than 5mm. In addition, more than 93% of the MAD values for CAESAR are significantly smaller than the ANSUR survey acceptable errors. Therefore, it is concluded that the type of scan-extracted measures used in CAESAR are as good as or better than comparable manual measurements. Scan-extracted measurements that do not use markers or are not straight-line distances are not represented here and additional studies would be needed to verify their precision.