Group presence in virtual worlds: Supporting collaborative e-learning

Even though e-learning endeavors have significantly proliferated in recent years, current e-learning technologies provide poor support for group-oriented learning. The now popular virtual world's technologies offer a possible solution. Virtual worlds provide the users with a 3D - computer generated shared space in which they can meet and interact through their virtual representations. Virtual worlds are very successful in developing high levels of engagement, presence and group presence in the users. These elements are also desired in educational settings since they are expected to enhance performance. The goal of this research is to test the hypothesis that a virtual world learning environment provides better support for group-oriented collaborative e-learning than other learning environments, because it facilitates the emergence of group presence. To achieve this, a quasi-experimental study was conducted and data was gathered through the use of various survey instruments and a set of collaborative tasks assigned to the participants. Data was gathered on the dependent variables: Engagement, Group Presence, Individual Presence, Perceived Individual Presence, Perceived Group Presence and Performance. The data was analyzed using the statistical procedures of Factor Analysis, Path Analysis, Analysis of Variance (ANOVA) and Multivariate Analysis of Variance (MANOVA). The study provides support for the hypothesis. The results also show that virtual world learning environments are better than other learning environments in supporting the development of all the dependent variables. It also shows that while only Individual Presence has a significant direct effect on Performance; it is highly correlated with both Engagement and Group Presence. This suggests that these are also important in regards to performance. Developers of e-learning endeavors and educators should incorporate virtual world technologies in their efforts in order to take advantage of the benefit they provide for e-learning group collaboration.

Collaborative mediation

A Mediation System utilizes a central security mediator that is primarily concerned with securing the internal structure of the Mediation System. The current problem is that clients are unable to have authority and administrative rights over the security of their data during a transaction. In addition, this Mediation System is unsuited in presenting a metric that measures the level of confidence of security access rights. This creates a black-box perspective from the client towards the Mediation System and also gives no assurance to these clients that they have assigned the proper security access rights that reflect the current environment of the mediation system. This dissertation presents a Collaborative Information System (CIS) that uses an agent based approach to encapsulate collaborative information and security policies within the Mediation System which are under the control of the clients of the Mediation System. In conjunction with the CIS's Stochastic Security Framework it is possible to take a probabilistic approach in modeling the security access rights of a collaboration transaction. The research results showed that it is feasible to construct a Mediation System utilizing agents and stochastic equations to establish an environment where the client has authority and administrative control in assigning security access rights to their collaborative data that can establish a metric that measures the level of confidence of these assigned rights.

Mitigating inconsistencies by coupling data cleaning, filtering, and contextual data validation in wireless sensor networks

With the advent of peer to peer networks, and more importantly sensor networks, the desire to extract useful information from continuous and unbounded streams of data has become more prominent. For example, in tele-health applications, sensor based data streaming systems are used to continuously and accurately monitor Alzheimer's patients and their surrounding environment. Typically, the requirements of such applications necessitate the cleaning and filtering of continuous, corrupted and incomplete data streams gathered wirelessly in dynamically varying conditions. Yet, existing data stream cleaning and filtering schemes are incapable of capturing the dynamics of the environment while simultaneously suppressing the losses and corruption introduced by uncertain environmental, hardware, and network conditions. Consequently, existing data cleaning and filtering paradigms are being challenged. This dissertation develops novel schemes for cleaning data streams received from a wireless sensor network operating under non-linear and dynamically varying conditions. The study establishes a paradigm for validating spatio-temporal associations among data sources to enhance data cleaning. To simplify the complexity of the validation process, the developed solution maps the requirements of the application on a geometrical space and identifies the potential sensor nodes of interest. Additionally, this dissertation models a wireless sensor network data reduction system by ascertaining that segregating data adaptation and prediction processes will augment the data reduction rates. The schemes presented in this study are evaluated using simulation and information theory concepts. The results demonstrate that dynamic conditions of the environment are better managed when validation is used for data cleaning. They also show that when a fast convergent adaptation process is deployed, data reduction rates are significantly improved. Targeted applications of the developed methodology include machine health monitoring, tele-health, environment and habitat monitoring, intermodal transportation and homeland security.

A generalized multidimensional index structure for multimedia data to support content-based similarity searches in a collaborative search environment

Since multimedia data, such as images and videos, are way more expressive and informative than ordinary text-based data, people find it more attractive to communicate and express with them. Additionally, with the rising popularity of social networking tools such as Facebook and Twitter, multimedia information retrieval can no longer be considered a solitary task. Rather, people constantly collaborate with one another while searching and retrieving information. But the very cause of the popularity of multimedia data, the huge and different types of information a single data object can carry, makes their management a challenging task. Multimedia data is commonly represented as multidimensional feature vectors and carry high-level semantic information. These two characteristics make them very different from traditional alpha-numeric data. Thus, to try to manage them with frameworks and rationales designed for primitive alpha-numeric data, will be inefficient. An index structure is the backbone of any database management system. It has been seen that index structures present in existing relational database management frameworks cannot handle multimedia data effectively. Thus, in this dissertation, a generalized multidimensional index structure is proposed which accommodates the atypical multidimensional representation and the semantic information carried by different multimedia data seamlessly from within one single framework. Additionally, the dissertation investigates the evolving relationships among multimedia data in a collaborative environment and how such information can help to customize the design of the proposed index structure, when it is used to manage multimedia data in a shared environment. Extensive experiments were conducted to present the usability and better performance of the proposed framework over current state-of-art approaches.

Filtering the international gender paradigm: Perspectives of gender in Barbados

My work presents a place-specific analysis of how gender paradigms interact across and within spatial scales: the global, the regional, the national and the personal. It briefly outlines the concepts and measures defining the international gender paradigm, and explores the filtration of this paradigm into assessments and understandings of gender and gender dynamics by and within Barbados. It does this by analyzing the contents of reports of the Barbados government to international bodies assessing the country’s performance in the area of gender equality, and by analyzing gender-comparative content of local print news media over the decade of the 1990s, and the first decade of the 2000s. It contextualizes the discussion within the realm of social and economic development. The work shows how the almost singular focus on “women” in the international gender paradigm may depreciate valid gender concerns of men and thus hinder the overall goal of achieving gender equality, that is, achieving just, inclusive societies.

Mitigating Inconsistencies by Coupling Data Cleaning, Filtering, and Contextual Data Validation in Wireless Sensor Networks

With the advent of peer to peer networks, and more importantly sensor networks, the desire to extract useful information from continuous and unbounded streams of data has become more prominent. For example, in tele-health applications, sensor based data streaming systems are used to continuously and accurately monitor Alzheimer's patients and their surrounding environment. Typically, the requirements of such applications necessitate the cleaning and filtering of continuous, corrupted and incomplete data streams gathered wirelessly in dynamically varying conditions. Yet, existing data stream cleaning and filtering schemes are incapable of capturing the dynamics of the environment while simultaneously suppressing the losses and corruption introduced by uncertain environmental, hardware, and network conditions. Consequently, existing data cleaning and filtering paradigms are being challenged. This dissertation develops novel schemes for cleaning data streams received from a wireless sensor network operating under non-linear and dynamically varying conditions. The study establishes a paradigm for validating spatio-temporal associations among data sources to enhance data cleaning. To simplify the complexity of the validation process, the developed solution maps the requirements of the application on a geometrical space and identifies the potential sensor nodes of interest. Additionally, this dissertation models a wireless sensor network data reduction system by ascertaining that segregating data adaptation and prediction processes will augment the data reduction rates. The schemes presented in this study are evaluated using simulation and information theory concepts. The results demonstrate that dynamic conditions of the environment are better managed when validation is used for data cleaning. They also show that when a fast convergent adaptation process is deployed, data reduction rates are significantly improved. Targeted applications of the developed methodology include machine health monitoring, tele-health, environment and habitat monitoring, intermodal transportation and homeland security.

Patterns of cooperation, conflict, and domination in children's collaborative problem-solving

This study examined the influence of age, expertise, and task difficulty on children's patterns of collaboration. Six- and eight-year-old children were individually pretested for ability to copy a Lego model and then paired with each other and asked to copy two more models. The design was a 3 (dyad skill level: novice, expert, or mixed) X 2 (age: six or eight) X 2 (task difficulty: moderate or complex) factorial. Results indicated that cooperation increased with age and expertise and decreased with task difficulty. However, expertise had a greater influence on younger than older children's interaction styles. It is argued that with age, social skills may become as important as expertise in determining styles of collaboration. The issue is raised of whether cooperation, domination, and independence represent developmental sequences (i.e., independence precedes cooperation) or whether they represent personal styles of interaction. Finally, it is suggested that an important goal for future research is to assess the relationship between patterns of collaboration and learning.

An Autoregressive Conditional Filtering Process to remove Intraday Seasonal Volatility and its Application to Testing the Noisy Rational Expectations Model

We develop a new autoregressive conditional process to capture both the changes and the persistency of the intraday seasonal (U-shape) pattern of volatility in essay 1. Unlike other procedures, this approach allows for the intraday volatility pattern to change over time without the filtering process injecting a spurious pattern of noise into the filtered series. We show that prior deterministic filtering procedures are special cases of the autoregressive conditional filtering process presented here. Lagrange multiplier tests prove that the stochastic seasonal variance component is statistically significant. Specification tests using the correlogram and cross-spectral analyses prove the reliability of the autoregressive conditional filtering process. In essay 2 we develop a new methodology to decompose return variance in order to examine the informativeness embedded in the return series. The variance is decomposed into the information arrival component and the noise factor component. This decomposition methodology differs from previous studies in that both the informational variance and the noise variance are time-varying. Furthermore, the covariance of the informational component and the noisy component is no longer restricted to be zero. The resultant measure of price informativeness is defined as the informational variance divided by the total variance of the returns. The noisy rational expectations model predicts that uninformed traders react to price changes more than informed traders, since uninformed traders cannot distinguish between price changes caused by information arrivals and price changes caused by noise. This hypothesis is tested in essay 3 using intraday data with the intraday seasonal volatility component removed, as based on the procedure in the first essay. The resultant seasonally adjusted variance series is decomposed into components caused by unexpected information arrivals and by noise in order to examine informativeness.

Content filtering and enrichment using triplets for text generation

Existe una cantidad enorme de información en Internet acerca de incontables temas, y cada día esta información se expande más y más. En teoría, los programas informáticos podrían beneficiarse de esta gran cantidad de información disponible para establecer nuevas conexiones entre conceptos, pero esta información a menudo aparece en formatos no estructurados como texto en lenguaje natural. Por esta razón, es muy importante conseguir obtener automáticamente información de fuentes de diferentes tipos, procesarla, filtrarla y enriquecerla, para lograr maximizar el conocimiento que podemos obtener de Internet. Este proyecto consta de dos partes diferentes. En la primera se explora el filtrado de información. La entrada del sistema consiste en una serie de tripletas proporcionadas por la Universidad de Coimbra (ellos obtuvieron las tripletas mediante un proceso de extracción de información a partir de texto en lenguaje natural). Sin embargo, debido a la complejidad de la tarea de extracción, algunas de las tripletas son de dudosa calidad y necesitan pasar por un proceso de filtrado. Dadas estas tripletas acerca de un tema concreto, la entrada será estudiada para averiguar qué información es relevante al tema y qué información debe ser descartada. Para ello, la entrada será comparada con una fuente de conocimiento online. En la segunda parte de este proyecto, se explora el enriquecimiento de información. Se emplean diferentes fuentes de texto online escritas en lenguaje natural (en inglés) y se extrae información de ellas que pueda ser relevante al tema especificado. Algunas de estas fuentes de conocimiento están escritas en inglés común, y otras están escritas en inglés simple, un subconjunto controlado del lenguaje que consta de vocabulario reducido y estructuras sintácticas más simples. Se estudia cómo esto afecta a la calidad de las tripletas extraídas, y si la información obtenida de fuentes escritas en inglés simple es de una calidad superior a aquella extraída de fuentes en inglés común.

Accuracy of a method based on atomic absorption spectrometry to determine inorganic arsenic in food : Outcome of the collaborative trial IMEP-41

Peer reviewed

Application of time series and Kalman filtering for structural health monitoring of a bridge

Peer reviewed

Learning Mechanisms for Information Filtering Agents

Postprint

Assessing the relative validity of the Scottish Collaborative Group Food Frequency Questionnaire for measuring dietary intake in adults

Peer reviewed

Ph.D. graduate student, Shelby Servais and REU, Julio Pachon conducted collaborative research in the wetland mesocosms during summer 2013.

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Seawater carbonate chemistry, sample density and Strongylocentrotus purpuratus size, filtering and respiration rate during experiments, 2011

Anthropogenic CO2 emissions are acidifying the world's oceans. A growing body of evidence is showing that ocean acidification impacts growth and developmental rates of marine invertebrates. Here we test the impact of elevated seawater pCO2 (129 Pa, 1271 µatm) on early development, larval metabolic and feeding rates in a marine model organism, the sea urchin Strongylocentrotus purpuratus. Growth and development was assessed by measuring total body length, body rod length, postoral rod length and posterolateral rod length. Comparing these parameters between treatments suggests that larvae suffer from a developmental delay (by ca. 8%) rather than from the previously postulated reductions in size at comparable developmental stages. Further, we found maximum increases in respiration rates of + 100 % under elevated pCO2, while body length corrected feeding rates did not differ between larvae from both treatments. Calculating scope for growth illustrates that larvae raised under high pCO2 spent an average of 39 to 45% of the available energy for somatic growth, while control larvae could allocate between 78 and 80% of the available energy into growth processes. Our results highlight the importance of defining a standard frame of reference when comparing a given parameter between treatments, as observed differences can be easily due to comparison of different larval ages with their specific set of biological characters.