The purpose of this investigation was to measure the direction community colleges across the United States and Canada have been taking in the development of technology based facilities and how the evolutional changes in the use and access of computers, multimedia, video and information resources for education has had an impact on facility development. To gain an insight on technology based facilities remodeling and/or development, an instrument of forty questions was developed and mailed to the facility offices and technology developers at the fifty-five (N=55) community colleges that are members of the League for Innovation in the Community College. The thirty-four (34) responses from the survey were tabulated using a Modified-Delphi Technique to determine a consensus of direction on seven conceptual issues: facility function, funding, human resources, educational activities, equipment, benefits and critical issues. The results of the investigation suggested: I) developing facilities for broad use; 2) the design for most new facilities should consider future modification possibilities; 3) there is an insignificant change in the number of newly constructed or planned joint-use, multipurpose technology based facilities; 4) most institutions fund technology at a rate between 5% and 9% of the institutions total operating budget; 5) there is no common strategy for developing the human resources needed for new facilities; 6) almost all community colleges do not require any form of technology literacy for employees; 7) the most innovative facilities developed include: open curriculum support centers, joint use facilities, computer courtyards, expanded libraries, instructional learning centers, facilitated learning centers, and technology support centers; 8) most community colleges have strategic plans and operational plans to support them; 9) very few empirical studies have been conducted at community colleges on the value of technology based facilities; and, 10) only two-thirds of facility development projects are based on using new methods of instruction or learning paradigms.

Educators, legislators, parents, and others with a stake in public education need reliable evidence that the investment of time and money in instruction with technology results in measurable gains leading to higher student achievement. Students making the transition from elementary to middle school are especially sensitive to changes in the learning environment and are at risk for school failure. This study investigated the relationships between student participation in elementary self-contained fifth grade multimedia classrooms and achievement and adjustment to the middle school setting. Grade point averages, average daily attendance, and scores on a standardized achievement test were compared for students attending multimedia fifth grade classrooms and all other students after the transition to sixth grade at one middle school in Orlando, Florida. Findings were compared using a one-tailed independent t-test for independent samples, and the Mann Whitney U-test, the non-parametric analogue of the t-test for data sets which are not normally distributed. Two surveys of student technology use and satisfaction with learning in a technology rich environment were administered. Sixth grade teachers were also surveyed about their levels of use and comfort with teaching with technology. Although the means for grade point averages, average daily attendance, and selected subtests of the Stanford-8 Achievement Test were higher for the students attending multimedia fifth grades, the results were not statistically significant. Student survey results were discussed, which indicated that students valued opportunities to use technology and were motivated to complete learning tasks that involved technology. The small number of middle school teachers who were surveyed indicated that while their interest in using technology in the classroom was high, the average 50-minute class period was too short to accomplish much with technology. Additional studies were recommended, to include studies that defined more clearly the conditions in which technology is used to support effective and efficient learning and problem solving.

Web applications are collections of files for the purpose of responding to user requests in client-server mode over the World Wide Web. Most web applications have a connection to one or more databases for storing and delivering data as required by user requests. Some include a web server extension. There are a number of such systems, each belonging to a set of archetypes. Important factors in selecting a web application include conformance to principles of software engineering, ability to enforce application-level security, and resource conservation or high performance. Existing experimental studies do not provide the required data, owing to variations in configuration, aggregated, statistically mixed workloads, and lack of numerical performance criteria for the application alone. This research gained important insights from the application of scientific methods and numerical, objective criteria to the comparison of web applications. This project conducted architectural analyses, security tests and performance measurements of selected archetypes. This work assessed state saving, separation of concerns, security and performance with standardized tests administered in an invariant environment, i.e., host hardware, operating system, web server and database server. Architectural analysis was applied to state maintenance and separation of concerns. Interactive simulations tested abilities of archetypes to enforce security and to protect themselves from hacker attacks. Automated load tests measured resources consumed and application latency as a function of HTML request traffic. HTML page requests consisted of a standard set of queries against the same dataset. A load test application was developed to manage the testing and data-recording automatically. Results consist of rankings of web application archetypes according to a metric developed for this purpose. Supporting data consist of graphs of latency vs. time and page request traffic vs. resources consumed. Results show the capability of archetypes to enforce security, maintain data integrity, conserve server resources, and to make timely and efficient delivery of client-requested data

Computer network attacks seek to achieve one or more objectives against the targeted system. The attack may be designed to gain access to sensitive data, modify records, or conduct activities designed to deny authorized users access to system resources. An effective defense against these incidents requires both the timely and accurate detection of the events and a response to the incident that mitigates the damage caused by the attack. While there is an increasing need for a system capable of accurately identifying network attacks there are very few effective methods capable of detecting these incidents. The constantly changing nature of network attacks requires a flexible defensive system that is capable of analyzing the enormous amount of network traffic and identifying attacks from the available data. The ability to effectively respond to an attack after it has been detected is also very limited. As a result, a rapid and well-organized attack can result in substantial damage to a targeted system before defensive measures can be activated. The goal of this research was the design of an innovative approach to the protection of computer networks that used adaptive neural network techniques to identify and respond to attempts to deny authorized users access to system resources. Since it is impossible to represent all of the possible system states and types of attacks that could occur the ability of the neural network-based system to adapt to changes in the network environment depended upon an incremental learning capability that was developed as part of this research. The adaptive neural network system incorporated a modified reinforcement learning approach to enhance the identification of new network attacks. This capability allowed the intrusion detection system to autonomously improve its analytical ability in response to changes in the threats against the protected network and then take an action that minimized the damage to the protected system. A prototype adaptive neural network architecture was implemented and evaluated in a simulated computer network environment.

Computation of marginal probabilities in Bayesian Belief Networks is central to many probabilistic reasoning systems and automatic decision making systems. The process of belief updating in Bayesian Belief Networks (BBN) is a well-known computationally hard problem that has recently been approximated by several deterministic algorithms and by various randomized approximation algorithms. Although the deterministic algorithms usually provide probability bounds, they have exponential runtimes. Some of the randomized schemes have a polynomial runtime, but do not exploit the causal independence in BBNs to reduce the complexity of the problem. This dissertation presents a computationally efficient and deterministic approximation scheme for this NP-hard problem that recovers approximate posterior probabilities given a large multiply connected BBN. The scheme presented utilizes recent work in belief updating for BBNs by Santos and Shimony (1998) and Bloemeke (1998). The scheme employs the Independence-based (IB) assignments proposed by Santos and Shimony to reduce the graph connectivity and the number of variables in the BBN by exploiting causal independence. It recovers the desired posterior probabilities by means of Netica™, a commercially available application for Belief Networks and Influence Diagrams.

Tactical asset allocation (T AA) is an investment strategy that switches an investment among different types of asset classes such as stocks, bonds or cash. The strategy consists of identifying the assets with the best performance potential within a defined short period of time. Artificial neural networks (ANN) have been successfully used to model nonlinear systems such as stock and bond price series. ANN have been used to forecast stock and bonds prices. The goal of most investment managers is to beat the market. This means to outperform an index representative of a specific overall market. The objective of T AA is to switch an investment to the asset class that will yield the best performance during the upcoming period. The success of TAA depends on the accuracy of the prediction of which asset class will yield the best performance. The purpose of this dissertation project was to investigate the effectiveness of artificial neural networks in forecasting the probability that one asset class would outperform two others by the end of a 30-day period. The asset classes considered were stocks, bonds and money market in the United States. An ANN was trained with fundamental and technical historical data. ANNs with different topologies were trained before arriving at an optimal configuration. A three-layer feedforward neural network offered the lowest generalization error. The selected ANN was trained to forecast the probabilities of each of the three investment asset classes outperforming the other two. With the forecast probabilities two TAA portfolios were created. The first portfolio was 100% invested on the asset class with the highest probability of outperforming the other two. The second portfolio followed a risk-neutral tactical asset allocation strategy based on the forecast probabilities. The accumulated returns at the end of the test period were compared to four benchmarks that represented buy-and-hold strategies. The first T AA portfolio outperformed all benchmarks. The risk-neutral portfolio outperformed all but the accumulated returns of the S&P500 index. The tests proved that an ANN is effective in forecasting the probabilities of one asset class outperforming others. Also, the results were used to create portfolios that outperformed the benchmarks.

This research provides an advance in the application of wireless, ad hoc networks to the domain of distributed, real-time applications. Traditionally, wireless communications are not deployed within real-time systems, the attributes of wireless protocols tend to run counter to the temporal requirements of real-time systems. When wireless protocols have been used in real-time systems, the application tends to be limited to systems for which there exists a priori knowledge of the network structure or the network communication. This research provides a model (or methodology) for evaluating the extent by which Bluetooth supports deterministic communication, thus allowing system engineers the ability to validate Bluetooth's ability to support real-time deadlines within software applications based on asynchronous communication. This research consisted primarily of an evaluation of the applicability of Bluetooth protocols to asynchronous real-time communication. The research methodology consisted of three distinct stages of research and development. The first stage of the study comprised the development of an analytical model describing the expected behavior of Bluetooth's ACL transmissions and the ability of ACL data packets to meet real-time deadlines. During the second stage of the study, the focus turned to the implementation of the Bluetooth HCI and L2CAP protocol layers. This implementation served as a test harness to gather actual performance data using commercial Bluetooth radios. The final stage of the study consisted of a comparative analysis of the predicted behavior established during the first stage of the study and the actual behavior experienced using the Bluetooth implementation from the second stage of the study. The analysis demonstrates the effectiveness of the model (from the first stage) by measuring the model's ability to accurately predict a piconet's ability to meet real-time deadlines for asynchronous communication when measured at the HCI-L2CAP protocol layer boundary.

Many people in educational institutions, governmental departments, and training organizations are searching for innovative methods to provide quality education for training students. Under the system of traditional education, educators have tried hard to find innovative and effective means to overcome some educational problems. Students are unable to attend classes due to some situational barriers such as time, transportation, and location. As for institutional barriers, some schools do not have adequate professional curriculums and proficient instructors to reach educational goals. In that case, it is necessary for these schools to hire facu1ty from the advanced educational institutions overseas such as American and English universities. Many educational workers are considering the use of technology-based distance learning system to meet those needs. By using the following five computer technologies delivery tools: 1) ISDN-based videoconferencing, 2) the compressed video/audio recording, 3) the Internet, 4) the World-Wide Web, and 5) the electronic mail, a multimedia distance education system will be established between countries throughout the world. All of the constructional phases of a combined distance learning system are highly interactive; each has an impact on the others. Likewise, information of target students' feedback obtained from an evaluation phase will have a direct influence on how future iterations of the distance learning media are designed and carried out. Based on an existing experimental combined computer-technology distance learning system between the College of Pharmacy of NSU, Florida and the National Defense Medical College, Taiwan, the goal of the research is developing a set of quantitative metric to evaluate the distance learning system. A fifty-five-item questionnaire had been filled out by all target students. The contribution of this evaluation is not only to elicit reactions from target students for systems satisfaction levels and educational delivery problems, but to give the researcher critical strengths or weaknesses on the useful and appropriate guidelines for future distance learning system refining.

This study examined the effect of a computerized Decision Support System (DSS) on sixth grade students' achievement and motivation. The study attempted to increase student motivation and achievement utilizing interactive graphical feedback and textual motivational messages, through the use of a computerized Decision Support System that was designed expressly for the study. The population for the study was students enrolled in one south Florida school. Comparisons were made between two groups: one textual feedback group and one graphical group. Students entered their test grades into the DSS, which provided them with their grade point averages. The textual feedback group used a simple entry/output form of the DSS and received textual information about their grade point average. The graphical feedback group used the same DSS which also contained additional motivational messages and an interactive graphical representation of their grades, updated with each entry. Both groups had the ability to formulate future possibilities of grades. It was hypothesized that the graphical and motivational feedback would increase the intrinsic motivation of the students, and thereby boost their achievement. A spelling achievement test was developed from spelling words provided by the teacher partaking in the study. Spelling tests were given to the students at the beginning of the study, and again at the end of the study to allow correlation of the data collected. Students' intrinsic motivation was measured with data obtained through the utilization of a special questionnaire designed for the study, which was given as both a pre-test and a post-test. This research indicates that a student's intrinsic motivation is positively affected by the receipt of feedback through the utilization of a computer decision support system. By integrating the student into the decision making process, a student will be motivated to achieve, which demonstrates a distinct relationship between intrinsic motivation and spelling achievement.

The online software dealer store project of the Electronic Software Dealer (ESD) had a problem of low sales volume, and the problem was attributed to several design problems. Literature indicated that web page design might affect the performance of a commercial web site; however, how the design factors influenced the success of the web site was unknown. To bridge this knowledge gap, a case study research method was used with a methodology that adopted the techniques of Helmstetter (1997), the web guidelines of Dellino (1996), Heller and Rivers (1996), and the Object-Action Model (OAl) of Shneiderman (1997) to analyze the research problem. In order to achieve the objectives, the researcher also analyzed the business models and design success-factors of Dow Jones & Company's Wall Street Journal Interactive Edition (WSJIE) site. In addition to the analysis, the researcher reviewed the literature related to commercial web site projects, interviewed the programmer involved in the ESD project, evaluated the design of ESD's online software store, and designed a prototype for the suggested solution. Results showed that the traditional system development life cycle (SDLC) still plays an important role in commercial web projects, and emphasis on planning, analysis, and the maintenance stages safeguards a more successful implementation of the project. Additionally, a design focusing on business models, marketing niches, and users ensures repeat visits and secures a more financial success to the commercial web site. This study focused on the design success-factors of commercial web sites, and did not cover issues of security and performance of the Web server.