<p>  </p> <p>Leadership style research spans many areas both inside and outside of education. This study sought to add to that research by looking at the leadership of technology directors in education. This population was targeted because gaps in student abilities versus performance needs have only expanded over the course of the COVID-19 pandemic and the number of teachers available to meet those needs has decreased in many areas. To address this need, technology has been increasingly the source that administrators are turning to in an attempt to do more with less. The question that arises from this is simple: how ready are teachers to use new technologies to meet those needs? There are many factors that may impact this, but a key one this study seeks to answer is how the interactions between a technology director and teachers impacts their readiness to do that. Leadership styles were collected using the Multifactor Leadership Questionnaire (MLQ) and teacher readiness was collected using a modified version of the Stages of Concern Questionnaire (SOCQ). Basic demographic data were also created to see if setting of a school or generational age of the participants impacted the results. SOCQ measures of central tendency were compared via MLQ scores and showed no results significant enough to support a connection between these measures. Small sample size was a major issue for this analysis and likely had a significant impact on these findings.</p>

Automatic speech recognition (ASR) technology has matured over the past few decades and has made significant impacts in a variety of fields, from assistive technologies to commercial products. However, ASR system development is a resource intensive activity and requires language resources in the form of text annotated audio recordings and pronunciation dictionaries. Unfortunately, many languages found in the developing world fall into the resource-scarce category and due to this resource scarcity the deployment of ASR systems in the developing world is severely inhibited. In this thesis we present research into developing techniques and tools to (1) harvest audio data, (2) rapidly adapt ASR systems and (3) select “useful” training samples in order to assist with resource-scarce ASR system development. We demonstrate an automatic audio harvesting approach which efficiently creates a speech recognition corpus by harvesting an easily available audio resource. We show that by starting with bootstrapped acoustic models, trained with language data obtain from a dialect, and then running through a few iterations of an alignment-filter-retrain phase it is possible to create an accurate speech recognition corpus. As a demonstration we create a South African English speech recognition corpus by using our approach and harvesting an internet website which provides audio and approximate transcriptions. The acoustic models developed from harvested data are evaluated on independent corpora and show that the proposed harvesting approach provides a robust means to create ASR resources. As there are many acoustic model adaptation techniques which can be implemented by an ASR system developer it becomes a costly endeavour to select the best adaptation technique. We investigate the dependence of the adaptation data amount and various adaptation techniques by systematically varying the adaptation data amount and comparing the performance of various adaptation techniques. We establish a guideline which can be used by an ASR developer to chose the best adaptation technique given a size constraint on the adaptation data, for the scenario where adaptation between narrow- and wide-band corpora must be performed. In addition, we investigate the effectiveness of a novel channel normalisation technique and compare the performance with standard normalisation and adaptation techniques. Lastly, we propose a new data selection framework which can be used to design a speech recognition corpus. We show for limited data sets, independent of language and bandwidth, the most effective strategy for data selection is frequency-matched selection and that the widely-used maximum entropy methods generally produced the least promising results. In our model, the frequency-matched selection method corresponds to a logarithmic relationship between accuracy and corpus size; we also investigated other model relationships, and found that a hyperbolic relationship (as suggested from simple asymptotic arguments in learning theory) may lead to somewhat better performance under certain conditions. / Thesis (PhD (Computer and Electronic Engineering))--North-West University, Potchefstroom Campus, 2013.

Automatic speech recognition (ASR) technology has matured over the past few decades and has made significant impacts in a variety of fields, from assistive technologies to commercial products. However, ASR system development is a resource intensive activity and requires language resources in the form of text annotated audio recordings and pronunciation dictionaries. Unfortunately, many languages found in the developing world fall into the resource-scarce category and due to this resource scarcity the deployment of ASR systems in the developing world is severely inhibited. In this thesis we present research into developing techniques and tools to (1) harvest audio data, (2) rapidly adapt ASR systems and (3) select “useful” training samples in order to assist with resource-scarce ASR system development. We demonstrate an automatic audio harvesting approach which efficiently creates a speech recognition corpus by harvesting an easily available audio resource. We show that by starting with bootstrapped acoustic models, trained with language data obtain from a dialect, and then running through a few iterations of an alignment-filter-retrain phase it is possible to create an accurate speech recognition corpus. As a demonstration we create a South African English speech recognition corpus by using our approach and harvesting an internet website which provides audio and approximate transcriptions. The acoustic models developed from harvested data are evaluated on independent corpora and show that the proposed harvesting approach provides a robust means to create ASR resources. As there are many acoustic model adaptation techniques which can be implemented by an ASR system developer it becomes a costly endeavour to select the best adaptation technique. We investigate the dependence of the adaptation data amount and various adaptation techniques by systematically varying the adaptation data amount and comparing the performance of various adaptation techniques. We establish a guideline which can be used by an ASR developer to chose the best adaptation technique given a size constraint on the adaptation data, for the scenario where adaptation between narrow- and wide-band corpora must be performed. In addition, we investigate the effectiveness of a novel channel normalisation technique and compare the performance with standard normalisation and adaptation techniques. Lastly, we propose a new data selection framework which can be used to design a speech recognition corpus. We show for limited data sets, independent of language and bandwidth, the most effective strategy for data selection is frequency-matched selection and that the widely-used maximum entropy methods generally produced the least promising results. In our model, the frequency-matched selection method corresponds to a logarithmic relationship between accuracy and corpus size; we also investigated other model relationships, and found that a hyperbolic relationship (as suggested from simple asymptotic arguments in learning theory) may lead to somewhat better performance under certain conditions. / Thesis (PhD (Computer and Electronic Engineering))--North-West University, Potchefstroom Campus, 2013.

由於科技的進步與國際化的趨勢，企業正面臨著充滿競爭、多變與相互結盟的經營環境，組織必須跳脫出過去沈重而僵化的運作體制，在組織結構與經營模式上加注更多的彈性與應變能力，資訊科技對於大量資料的處理、儲存、傳遞功能使得現代企業日益重視此項工具所能帶給組織的高附加價值。 　　本研究主要是探討資訊科技與組織競爭優勢的因果關係，研究的目的在於了解「資訊科技應如何與組織所擁有的核心能力相配合，以創造組織的競爭優勢。」，並在此目的下深入探討以下三個問題：資訊科技與核心能力之間的配合型態所代表的涵義以及企業界實際的作法為何？資訊科技與核心能力的最適搭配是否會因組織的特性而有所差異？核心能力與資訊科技之間的配合型態對競爭優勢的影響為何？ 　　本研究屬於探索個案研究，主要是藉由實地訪談國內電腦化績效卓越的公司，配合國內外相關文獻，以探討資訊科技的功能特性與組織核心能力的各種搭配型態，並進一步討論在不同的組織價值目標引領下，資訊科技與組織核心能力的最適配合型態與競爭優勢的因果關係。 　　經由理論與實務的印証，本研究發現透過資訊科技的收集、處理累積與分享傳遞的功能，能大幅降低企業流程的溝通協調成本，組織藉由知識與經驗的累積而能發揮「社會記憶」的功能，並經由不斷地對顧客進行深入的了解與探尋使組織比競爭者更有能力去滿足客戶的需求，如此組織便能以更具效率的運作程序及更高的顧客化程度來達成其所欲追求的競爭優勢。

The purpose of this research was to examine the role of instructional technology resource teachers (ITRTs) within Virginia's public school divisions focusing on how ITRTs used their time throughout the school year to integrate technology into the curriculum. Based on data from surveys of current ITRTs, the researcher investigated further to find relationships, if any, among the professional and educational backgrounds and work calendar of these teachers and their responses to their actual role. The study also addressed training that the ITRTs have received to assist them in their job duties and explored the participants' perceptions of their roles as ITRTs. Data were collected through the administration of an online survey sent to 1,199 ITRTs in 133 school divisions (districts) in Virginia. The response rate was 82% or 983 returns. The data were analyzed and presented using a tabular format along with a brief description. Based on the findings, 40.9% of the respondents listed Instructional Technology Resource Teacher as their official job title. The majority of respondents held master's degrees and teacher's licenses. Respondents reported that 95% were full-time ITRTs. Most worked on a 10- or 11-month work calendar. The findings showed that instructional technology resource teachers were assisting teachers somewhat with technology integration, but the time spent on solving software (64.8%) and hardware (53.3%) problems remains a concern. The majority stated that they had received training from their school divisions. The analysis showed that only 1.6% of the respondents had no training. Respondents overwhelmingly agreed that the most effective way to meet each school's instructional technology needs is to have one full-time instructional technology resource teacher in each school. Respondents stated there was not enough time allotted for teachers to plan for technology in the classroom and that there were insufficient funds for hardware and software needed for implementing technology into the classroom. Most agreed that support from school division administrators are assisting teachers in successfully integrating technology into the classroom and the majority of respondents disagreed that Standards of Learning (SOL) prompt teachers to use technology as a daily instructional tool.

Metall- und Maschinenbauunternehmen müssen im Durchschnitt pro Jahr ca. zwei Prozent ihres Umsatzes für Strom und Erdgas ausgeben und die Unternehmer gehen von weiteren Preissteigerungen aus. Damit rückt das Thema Energieeinsparung stärker denn je in den Fokus und wird zu einem strategischen Faktor. Um Kosten zu sparen und Wettbewerbsvorteile zu sichern, ist es notwendig, zielgenaue Energieeinsparmaßnahmen einzuleiten. Die ersten Maßnahmen, welche die meisten Maschinenbauunternehmen umsetzen, sind die Erneuerung der Beleuchtungs-, Heizungs- und Lüftungsanlage, die Verbesserung der Drucklufterzeugung sowie die thematische Sensibilisierung der Mitarbeiter. Aber auch in Maschinen mit ihren dazugehörigen elektrischen Antrieben, Lüftern und Aggregaten verbirgt sich eine große Menge an Optimierungspotenzial. Allerdings ist es hier notwendig nicht die Verbraucher im Einzelnen, sondern die Maschine und deren Prozesse im Ganzen zu betrachten. Meist fehlen hierfür aber geeignete Schnittstellen, um die Messwerte von Sensoren (bspw. Temperatur-, Drucksensoren, etc.) und Antrieben auslesen zu können, was dazu führt, dass diese Potenziale nicht ausgeschöpft werden.

The average metal and mechanical engineering company must spend around two per cent of its annual turnover on electricity and natural gas, and companies are expecting further price increases. As a result, the issue of energy saving is becoming more of a strategic factor than ever before. In order to save costs and ensure competitive advantages, it is necessary to introduce precise energy-saving measures. The first steps taken by most mechanical engineering companies are to replace lighting, heating, and ventilation systems, improve compressed air generation and raise employee awareness. However, there is also a great potential for optimization in machines with their individual electrical drives, fans, and units. In this case, though, it is necessary to look at the machine and its processes as a whole rather than the individual electrical energy consumers. In most cases, however, there is a lack of suitable interfaces for analyzing the measured values from sensors (e.g. temperature, pressure sensors, etc.) and drives, which concludes that this potential is not fully exploited.