Developmental Sentence Scoring (DSS; Lee 1974) is a well established, structured method for analyzing a child's expressive syntax within the context of a conversational speech sample. Automated DSS programs may increase efficiency of DSS analysis; however the program must be accurate in order to yield valid and reliable results. A recent study by Sagae, Lavie, and MacWhinney (2005) proposed a new method for analyzing the accuracy of automated language analysis programs. This method was used in addition to previously established methods to analyze the accuracy of a new automated DSS program, entitled DSSA (Channell, 2006). Previously collected language samples from 118 children between the ages of 3 and 11 years in age were manually and automatedly coded for DSS. The overall accuracy of DSSA was about 86%, while the mean point difference was approximately .7. DSSA generally scored language samples of children achieving lower manual DSS scores or children with language impairment with less accuracy than those of other children. While some precautions may need to be taken, accuracy levels are sufficiently high to allow the fully automated use of DSSA as an alternative to manual DSS scoring.

The purpose of this study was to examine the accuracy of English-speakers in determining the word recognition score of native Taiwan Mandarin-speakers. Digitally recorded Mandarin word lists were presented to 10 native Mandarin-speakers from Taiwan (five male, five female), from whom oral and written responses were collected. Oral responses were scored by 30 native English-speakers, 15 of which had no experience with Mandarin and 15 with two to three years of college-level Mandarin courses or equivalent knowledge of Mandarin. The judges who had experience with Mandarin were able to score the WRS tests with 97% accuracy (with scores ranging from 10% below to 4% above the actual score of the test). The judges without experience with Mandarin scored the WRS tests with 88.8% accuracy (with scores ranging from 34% below to 26% above the actual score of the test). An analysis of variance found that there was a significant difference between a judge's knowledge of Mandarin and his or her ability to accurately score the oral responses. An inspection of the performance of the judges in respect to the five different Mandarin tones indicated that there are some tone combinations that are more difficult to score correctly than others. While it is apparent that tone combination may play a role in the ability to accurately score WRS words in Mandarin, the implications of this for a clinical setting are uncertain because words with these tone combinations were not heard often. Tone perception training for the judges or simply making clinicians aware of this difficulty in tone identification may be of benefit in overcoming this obstacle.

Digital marketing has become an important part for companies in the process of attracting new customers. The digitalisation has led to substantial changes in the ways consumers and businesses search for information and do their research before making a purchase. A major shift has been observed regarding how digital communication influences the purchasing decision within the B2B sector. The purpose of this research project was to examine lead scoring in the perspective of B2B marketing automation, and to investigate how lead scoring can contribute to a more efficient and effective marketing process for Visma Advantage. The aim was to develop a recommendation for an optimal lead scoring model for Visma Advantage and which marketing actions that are suitable for the different lead score values. The result shows that there can be several ways of an optimal use of a lead scoring model for Visma Advantage. The result also presents a completed lead engagement scorecard and lead profile scorecard, together with a lead score matrix that specifies the thresholds for each lead score value. In addition to that, relevant marketing actions for each lead score value in the model is presented in the result. / Den digitala marknadsföringen har kommit till att bli en viktig del för företag, i processen avatt attrahera nya kunder. Digitaliseringen har lett till betydande förändringar av hurkonsumenter och företag söker information och gör sin efterforskning innan de slutför ettköp. Ett stort skifte har observerats, gällande hur digital kommunikation påverkarinköpsbeslutet, inom B2B-sektorn. Syftet med detta projekt var att undersöka lead scoring i perspektivet av automatisering avB2B marknadsföring, och att undersöka hur lead scoring kan bidra till en mer effektiv ochverkningsfull marknadsföring för Visma Advantage. Syftet var att ta fram en rekommendationför en optimal lead scoring-modell för Visma Advantage och vilka marknadsföringsåtgärdersom är passande för respektive lead score-värde. Resultatet visar att det kan finnas flera sätt för en optimal användning av en leadscoring-modell för Visma Advantage. Resultatet presenterar också ett färdigt poängkort förbåde profil-aspekten och för engagemangs-aspekten, tillsammans med en lead score-matrissom anger tröskelvärdena för varje lead score-värde. I tillägg till poäng-korten presenterasrelevanta marknadsföringsåtgärder för varje lead score-värde i modellen, i resultatet.

This study evaluated the effectiveness of a school-based training that attempted to improve the reliability of holistic scores teachers assigned to the writings of elementary school students. Seventeen teachers at one suburban elementary school located in the Southeastern United States participated in three training sessions that allowed for scoring practice and group discussions. The trainers, or presenters, were "faculty-experts." A comparison of scores the participants assigned to students' writings before and after the training was conducted. The analyses included t-tests that compared the participants' mean scores to the scores assigned by raters from the state, a within-group analysis of reliability as measured by Cronbach's Alpha, and percentage agreement analyses. The results suggested that the in-house training activities promoted higher inter-rater reliability of scores assigned to students' writings by the teachers in this study. This study also compared teachers identified as being highly confident writers with teachers who reported low levels of self-confidence related to writing. Prior to the training, the highly confident teachers' scores tended to be lower than the state scores and the scores assigned by their less confident peers. During group discussions, however, the "high-confidence" group was just as likely to change their scores to a higher level as to a lower level, and by the end of the training, both groups demonstrated more consistent score patterns.

Clinical Assisted Reproductive Technology (ART) practices seek to make improvements in embryo quality and resultant procedural success rates. There is a significant variance in live birth rates among clinics nationwide. The goal of this thesis is make comparisons of embryo quality among clinics and understand these differences. This analysis focuses on the stage between egg retrieval and embryo transfer. Because the currently accepted embryo scoring methods are not directly proportional to performance, a new scoring methodology is proposed and applied. Data provided by the Society for Assisted Reproductive Technology (SART) consisting of 36,836 patient cycles from 40 anonymous clinics nationwide is considered. After necessary reductions are made, the data is anatomized to link each embryo transferred to an implantation probability. A score is generated for each morphology grouping based on the average implantation rate of that group. This score is used as the basis for clinic comparisons. Top-performing clinics (in terms of live birth rates in patients agedold) are then shown to both produce embryos of higher score and achieve better results from embryos of identical morphology.

Cisco 2014 Annual Security Report clearly outlines the evolution of the threat landscape and the increase of the number of attacks. The UK government in 2012 recognised the cyber threat as Tier-1 threat since about 50 government departments have been either subjected to an attack or a direct threat from an attack. The cyberspace has become the platform of choice for businesses, schools, universities, colleges, hospitals and other sectors for business activities. One of the major problems identified by the Department of Homeland Security is the lack of clear security metrics. The recent cyber security breach of the US retail giant TARGET is a typical example that demonstrates the weaknesses of qualitative security, also considered by some security experts as fuzzy security. High, medium or low as measures of security levels do not give a quantitative representation of the network security level of a company. In this thesis, a method is developed to quantify the security risk level of known and unknown attacks in an enterprise network in an effort to solve this problem. The identified vulnerabilities in a case study of a UK based company are classified according to their severity risk levels using common vulnerability scoring system (CVSS) and open web application security project (OWASP). Probability theory is applied against known attacks to create the security metrics and, detection and prevention method is suggested for company network against unknown attacks. Our security metrics are clear and repeatable that can be verified scientifically

The use of analytic rubrics remains popular in the field of writing assessment. Previous work in second-language writing assessment and other fields like economics suggest that ratings produced using this method may have lower reliability on average than other methods. Currently, there is little research on the reliability of ratings of creative writing, specifically creative writing authored by adults. This study evaluated the reliability of ratings from an analytic rubric against those produced by a comparative method called Randomly Distributed Comparative Judgment and the rater experience of the methods. The author administered a science fiction and fantasy contest in which 9 raters rated subsets of 47 total contest entries. Raters used both methods on two occasions for a total of four ratings per assigned artifact. The analytic rubric ratings were analyzed using the Many-Facets Rasch Model to model story, rater, occasion, and interaction effects. The comparisons from the RDCJ method were used in a proprietary version of the Bradley-Terry Model to calculate true scores and rater effects. Analysis showed rater effects in the ratings of both methods, though greater for those associated with the rubric model. The ratings from the rubric also contained occasion effects, but the RDCJ ratings did not. Interviews with the raters found that raters generally favored the RDCJ method, though some would have preferred a modified version. However, they all found the rubric less useful, even though many thought that it covered the generally accepted factors of good creative writing. These findings may influence practitioners' decisions when choosing a rating method for shorter works of creative writing, particularly in contexts like story contests or university admissions. However, rating of creative writing is an understudied field compared to academic writing, and more work is needed in the areas of reliability and rating.

Animal welfare, reproduction, and milk production can be negatively affected when dairy cattle experience heat stress. Dairy cows in southern latitudes spend nearly 4 to 6 months in a state of heat stress. Animal health professionals and dairy producers use changes in physiological responses and behavioral patterns of cows as a tool for identifying poor health and welfare in periods of heat stress. The objectives of this study were to monitor the effects of heat stress on grazing dairy cows provided with shade or sprinklers by comparing various physiological indices of heat stress, and to, design and utilize a heat stress scoring system to evaluate heat stress severity on grazing dairy cows with access to shade and sprinklers. Finally, the results were used to predict the accuracy of the scoring system with machine learning methods. The findings from this study provide a new tool to assess heat stress in dairy cows.

No description available.

No description available.