Audit report of Iowa State University of Science and Technology, Ames, Iowa, (Iowa State University) as of and for the years ended June 30, 2015 and 2014

Audit report of Iowa State University of Science and Technology, Ames, Iowa, (Iowa State University) as of and for the years ended June 30, 2015 and 2014

Audit report of Iowa State University of Science and Technology, Ames, Iowa, (Iowa State University) and its discretely presented component unit as of and for the years ended June 30, 2015 and 2014

Audit report of Iowa State University of Science and Technology, Ames, Iowa, (Iowa State University) and its discretely presented component unit as of and for the years ended June 30, 2015 and 2014

Technology educational affordance: Bridging the gap between patterns of interaction and technology usage

Peer-reviewed

Semantic Web applications: a framework for industry and business exploitation – What is needed for the adoption of the Semantic Web from the market and industry

In recent years, Semantic Web (SW) research has resulted in significant outcomes. Various industries have adopted SW technologies, while the ‘deep web’ is still pursuing the critical transformation point, in which the majority of data found on the deep web will be exploited through SW value layers. In this article we analyse the SW applications from a ‘market’ perspective. We are setting the key requirements for real-world information systems that are SW-enabled and we discuss the major difficulties for the SW uptake that has been delayed. This article contributes to the literature of SW and knowledge management providing a context for discourse towards best practices on SW-based information systems.

Promoting Metacognitive Skills in a CSCL environment: implications for instructional and technology design

This paper aims to better understand the development of students’ learning processes when participating actively in a specific Computer Supported Collaborative Learning system called KnowCat. To this end, a longitudinal case study was designed, in which eighteen university students took part in a 12-month (two semesters) learning project. During this time period, the students followed an instructional process, using some elements of KnowCat (KnowCat key features) design to support and improve their interaction processes, especially peer learning processes. Our research involved both supervising the students’ collaborative learning processes throughout the learning project and focusing our analysis on the qualitative evolution of the students’ interaction processes and on the development of metacognitive learning processes. The results of the current research reveal that the instructional application of the CSCL-KnowCat system may favour and improve the development of the students’ metacognitive learning processes. Additionally, the implications of the design of computer supported collaborative learning networks and pedagogical issues are discussed in this paper.

Thick XML client and technology of its construction

Nykyään kolmeen kerrokseen perustuvat client-server –sovellukset ovat suuri kinnostuskohde sekä niiden kehittäjille etta käyttäjille. Tietotekniikan nopean kehityksen ansiosta näillä sovelluksilla on monipuolinen käyttö teollisuuden eri alueilla. Tällä hetkellä on olemassa paljon työkaluja client-server –sovellusten kehittämiseen, jotka myös tyydyttävät asiakkaiden asettamia vaatimuksia. Nämä työkalut eivät kuitenkaan mahdollista joustavaa toimintaa graafisen käyttöliittyman kanssa. Tämä diplomityö käsittelee client-server –sovellusten kehittamistä XML –kielen avulla. Tämä lähestymistapa mahdollistaa client-server –sovellusten rakentamista niin, että niiden graafinen käyttöliittymä ja ulkonäkö olisivat helposti muokattavissa ilman ohjelman ytimen uudelleenkääntämistä. Diplomityö koostuu kahdesta ostasta: teoreettisesta ja käytännöllisestä. Teoreettinen osa antaa yleisen tiedon client-server –arkkitehtuurista ja kuvailee ohjelmistotekniikan pääkohdat. Käytannöllinen osa esittää tulokset, client-server –sovellusten kehittämisteknologian kehittämislähestymistavan XML: ää käyttäen ja tuloksiin johtavat usecase– ja sekvenssidiagrammit. Käytännöllinen osa myos sisältää esimerkit toteutetuista XML-struktuureista, jotka kuvaavat client –sovellusten kuvaruutukaavakkeiden esintymisen ja serverikyselykaaviot.

Political market and regulatory uncertainty: Insights and implications for integrated strategy

Managers can craft effective integrated strategy by properly assessing regulatory uncertainty. Leveraging the existing political markets literature, we predict regulatory uncertainty from the novel interaction of demand and supply side rivalries across a range of political markets. We argue for two primary drivers of regulatory uncertainty: ideology-motivated interests opposed to the firm and a lack of competition for power among political actors supplying public policy. We align three, previously disparate dimensions of nonmarket strategy - profile level, coalition breadth, and pivotal target - to levels of regulatory uncertainty. Through this framework, we demonstrate how and when firms employ different nonmarket strategies. To illustrate variation in nonmarket strategy across levels of regulatory uncertainty, we analyze several market entry decisions of foreign firms operating in the global telecommunications sector.

CLARIN: common language resources and technology infrastructure

Presentamos el proyecto CLARIN, un proyecto cuyo objetivo es potenciar el uso de instrumentos tecnológicos en la investigación en las Humanidades y Ciencias Sociales

New kernel functions and learning methods for text and data mining

Recent advances in machine learning methods enable increasingly the automatic construction of various types of computer assisted methods that have been difficult or laborious to program by human experts. The tasks for which this kind of tools are needed arise in many areas, here especially in the fields of bioinformatics and natural language processing. The machine learning methods may not work satisfactorily if they are not appropriately tailored to the task in question. However, their learning performance can often be improved by taking advantage of deeper insight of the application domain or the learning problem at hand. This thesis considers developing kernel-based learning algorithms incorporating this kind of prior knowledge of the task in question in an advantageous way. Moreover, computationally efficient algorithms for training the learning machines for specific tasks are presented. In the context of kernel-based learning methods, the incorporation of prior knowledge is often done by designing appropriate kernel functions. Another well-known way is to develop cost functions that fit to the task under consideration. For disambiguation tasks in natural language, we develop kernel functions that take account of the positional information and the mutual similarities of words. It is shown that the use of this information significantly improves the disambiguation performance of the learning machine. Further, we design a new cost function that is better suitable for the task of information retrieval and for more general ranking problems than the cost functions designed for regression and classification. We also consider other applications of the kernel-based learning algorithms such as text categorization, and pattern recognition in differential display. We develop computationally efficient algorithms for training the considered learning machines with the proposed kernel functions. We also design a fast cross-validation algorithm for regularized least-squares type of learning algorithm. Further, an efficient version of the regularized least-squares algorithm that can be used together with the new cost function for preference learning and ranking tasks is proposed. In summary, we demonstrate that the incorporation of prior knowledge is possible and beneficial, and novel advanced kernels and cost functions can be used in algorithms efficiently.

Quantitative market and sales forecast accuracy analysis

The main objective of this master’s thesis was to quantitatively study the reliability of market and sales forecasts of a certain company by measuring bias, precision and accuracy of these forecasts by comparing forecasts against actual values. Secondly, the differences of bias, precision and accuracy between markets were explained by various macroeconomic variables and market characteristics. Accuracy and precision of the forecasts seems to vary significantly depending on the market that is being forecasted, the variable that is being forecasted, the estimation period, the length of the estimated period, the forecast horizon and the granularity of the data. High inflation, low income level and high year-on-year market volatility seems to be related with higher annual market forecast uncertainty and high year-on-year sales volatility with higher sales forecast uncertainty. When quarterly market size is forecasted, correlation between macroeconomic variables and forecast errors reduces. Uncertainty of the sales forecasts cannot be explained with macroeconomic variables. Longer forecasts are more uncertain, shorter estimated period leads to higher uncertainty, and usually more recent market forecasts are less uncertain. Sales forecasts seem to be more uncertain than market forecasts, because they incorporate both market size and market share risks. When lead time is more than one year, forecast risk seems to grow as a function of root forecast horizon. When lead time is less than year, sequential error terms are typically correlated, and therefore forecast errors are trending or mean-reverting. The bias of forecasts seems to change in cycles, and therefore the future forecasts cannot be systematically adjusted with it. The MASE cannot be used to measure whether the forecast can anticipate year-on-year volatility. Instead, we constructed a new relative accuracy measure to cope with this particular situation.

Effects of institutional environment and technology development on payment choice

The purpose of this paper is to analyze the combination of institutional factors and technology advances as determinants of payment systems choice. The theoretical set up suggests that countries entering into a new institutional environment approach accepting group attitudes towards payment choices as a consequence of institutional pressure and technology development. We apply the results of the model to 2004 European Union enlargement process. Results confirm the relevance of both institutional environment and technology development in retail payment system decisions of newly acceded countries.