Anthony Joseph

Group dynamics and collaborative group performance

This work resulted from an investigation of the activities component of group dynamics in collaborative (or cooperative) groups for two undergraduate sections of computer architecture taught in spring 2002. A cooperative group is a special type of small group consisting of two to four persons. Collaborative learning as used here is a central component of the author devised integrative cooperative learning teaching technique. The hypothesis for this study is that the greater a students level of within group course related activities for the imposed course assignments, the higher the students relative course outcomes. To examine this hypothesis, an analysis of the relationship between a students group activity and course outcomes was done. The sources of data were the student coordinator assessments and student journals compiled throughout the semester. The results of the analyses of the 63 students in the two sections of the undergraduate computer architecture course substantiated the hypothesis that the more a student participates in out-of-class collaborative academic group activity, the greater the students achievement.

A Study of Apparel Dress Model Technology on the Web

Introduction Business-to-consumer (B2C) e-Commerce continues to grow in the United States. Retail revenue on the Internet channel has approximated

Under-sampled Weyl–Heisenberg expansions via orthogonal projections in Zak space

141 billion in 2004 (Alter, 2005), an increase from

The Wavelet and Fourier Transforms in Feature Extraction for Text-Dependent, Filterbank-Based Speaker Recognition

100 billion in 2003 (Chabrow, 2004). Apparel sales on the Internet channel have been estimated to be

Predictive Ability of the Interest Rate Spread Using Neural Networks

13 billion in 2005 (Tedeschi, 2005). Sales on the Web have comprised 7.7% of all revenue sales sources in 2005 (Gagnier, 2005), an increase from 6.6% of revenue sales in 2004 and 3.6% in 2002. Forecasts have indicated revenues on the Web will increase to

A Wavelet Packet and Mel-Frequency Cepstral Coefficients-Based Feature Extraction Method for Speaker Identification

172 billion in 2005 (Alter, 2005) and to

Forecasting Purchasing Managers’ Index with Compressed Interest Rates and Past Values

230 billion, and 10% and higher of all sales, in 2008 (Chabrow, 2004). Consumers on the Web have grown to be mainstream customers of this additional buying channel, as 5 million households will annually buy on the Web for their first time through 2008 (Chabrow, 2004). Retailers have to continue to invest in better e-Commerce design (Ricadela, 2005), in order to ensure increased buying on the Web. Apparel retailers have to be cognizant of buyer perceptions of service, risk, price, product and experience (Jarvenpaa & Todd, 1997), but in order to convert a consumer into a buying customer, retailers have to differentiate their design in an especially functional, engaging and compelling experience (Pullman & Gross, 2004). Differentiation in encountering a home page of a retailer Web site, searching and choosing a product from a catalog on the site, ordering and paying for the product, and contacting customer service if needed enables experience in apparel shopping on the Web (Sebastianelli, Tamimi, & Rajan, 2005). Studies indicate the evolution in the design of e-Commerce sites from that of experimentation, creation of value and growth through focus, to differentiation of experience and of relationship (Gabriele, Brohman, Watson, & Parasuraman, 2004; Prahalad & Ramaswamy, 2004). Innovation in the design of the apparel buying experience is indicated to give an edge to a retailer Web site. The focus of our study is to examine the importance of factors that contribute to a discernable differential in the experience of goal-focused shoppers on apparel retailer dress model Web sites. Literature Review Studies in the literature indicate factors of compatibility of lifestyle (Ratchford, Talukdar, & Lee, 2001), ease of use, ease of effort (Baty & Lee, 1995), fun (Goldsmith, 2000), playfulness (Liu, Armett, Capella, & Taylor, 2001), and risk. The factors are indicated as important in the design of differentiation in a generic shopping experience (Jarvenpaa & Todd, 1997). Wolfinbarger and Gilly (2001) indicate differentiation in the context of goal focusing. Goal focusing is further indicated to be deliberate, efficient, fast, rational and task-focused shopping (Hoffman & Novak, 1996). Goal-focused shoppers are frequently independent of the help of online sales and service staff and are indicated to have a clearly defined objective to buy already identified products on the Web without the help of staff (Moe & Fader, 2001). Wolfinbarger and Gilly (2001) introduce the following factors as important in goal-focused shopping on a retailer Web site: * accessibility and convenience, which affords ease in effort in buying apparel products on the site; * availability of information, which allows for organized, relevant and searchable specifications on the products sold on the site; * lack of sociality, which enables fulfillment for a goal-focused independent shopper to buy the products with or without service or sales help; and * selection, which enables sufficiency in the inventory of apparel products sold on the Web site. Such factors give an enhanced edge to a retailer sensitive to goal-focused apparel shoppers on its Web site. Other studies indicate factors of customization and personalization of apparel Web sites as important in the differentiation of a site (Drogan & Hsu, 2003). …

Forecasting Real Inventories and the Anomaly of Money Illusion

Abstract An algorithm is presented which orthogonally projects signals into integer under-sampled Weyl–Heisenberg subspaces. The algorithm operates by periodization–decimation operations in Zak space, and can be viewed as direct Zak space extension of classical signal space procedures underlying orthogonal projections of Fourier expansions, the basis of divide and conquer fast Fourier transform algorithms. The language of groups is used, which highlights the duality between time and frequency spaces and facilitates sampling rate conversion. Results of numerical experiments are included, suggesting that the algorithm can be used for arrival time estimation of a partially known signal.

The Treasury Bill Rate, the Great Recession, and Neural Networks Estimates of Real Business Sales☆

An important step in speaker recognition is extracting features from raw speech that captures the unique characteristics of each speaker. The most widely used method of obtaining these features is the filterbank-based Mel Frequency Cepstral Coefficients (MFCC) approach. Typically, an important step in the process is the employment of the discrete Fourier transform (DFT) to compute the spectrum of the speech waveform. However, over the past few years, the discrete wavelet transform (DWT) has gained remarkable attention, and has been favored over the DFT in a wide variety of applications. This work compares the performance of the DFT with the DWT in the computation of MFCC in the feature extraction process for speaker recognition. It is shown that the DWT results in significantly lower order for the Gaussian Mixture Model (GMM) used to model speech and marginal improvement in accuracy.

Comparing the Forecasts of Money Demand

Abstract Interest rates are commonly used as predictors of future economic conditions as measured by industrial production, real gross domestic product and real total business sales (RTBS), as well as through the prediction of recessions in the economy. Recession forecasting is mainly characterized by probit categorical analysis, and there appear to be hardly any neural network research in this area. This paper contributes to the recession forecasting literature using interest rate spreads (the difference between the average yields on 10 year U.S. Treasury bonds and on 3 month U.S. Treasury bills) to forecast the 2007 to 2009 recession with neural network models referenced against regression models. It is shown that neural network models out-performed regression models as evidenced by the R-squared and mean square error performance metrics. Unlike other studies, the change in interest rates is used to compute the interest rate spread. The targeted variable is RTBS. The interest rate spread variable was used to generate three input variables comprising 23, 26, and 29 month leads respectively over RTBS.