Vocabulary in a second language is an indispensable building block of all comprehension (Folse, 2006; Nation, 2006). Teachers in content area classes such as science, math, and social studies frequently teach content specific vocabulary, but are not aware of the obstacles that can occur when students do not know the basic words. Word lists such as the General Service List (GSL) were created to assist students and teachers (West, 1953). The GSL does not adequately take into account the high level of polysemy of many common English words, nor has it been updated by genre to reflect specific content domains encountered by secondary science students in today's high stakes classes such as chemistry. This study examines how many words of the first 1000 words of the GSL occurred in the secondary chemistry textbooks sampled, how often the first 1000 words of the GSL were polysemous, and specifically which multiple meanings occurred. A discussion of results includes word tables that list multiple meanings present, example phrases that illustrate the context surrounding the target words, suggestions for a GSL that is genre specific to secondary chemistry textbooks and that is ranked by meaning as well as type, and implications for both vocabulary materials and classroom instruction for ELLs in secondary chemistry classes. Findings are essential to second language (L2) researchers, materials developers, publishers, and teachers.

A recently proposed defense for the anonymity network Tor uses preload lists of domains to determine what should be cached in the Domain Name System (DNS) caches of Tor relays. The defense protects against attacks that infer what is cached in Tor relays. By having domains continuously cached (preloaded), the cache will become independent of which websites have been visited. The current preload lists contain useless domains and have room for improvement. The objective of this project is to answer the question of "How can we generate better preload lists?" and to provide improved methods for generating preload lists, with the ultimate goal of generating better preload lists that the Tor Project can benefit from.  We further developed existing tools to use web crawling to find more useful domains, as well as implementing filtering to remove useless domains from the preload lists. The results of this project showed promising results, as the useless domains decreased by an average of around 57% and more useful domains were found. / Ett nyligen föreslaget försvar för anonymitetsnätverket Tor använder preload listor av domäner för att avgöra vad som ska cachelagras i domännamnssystemets (DNS) cacher för Tor reläer. Försvaret skyddar mot attacker som avgör vad som cachelagras i Tor relärer. Genom att ha domäner kontinuerligt cachade (förladdade), blir cachen oberoende av vilka websidor som har besökts. De nuvarande preload listorna innehåller värdelösa domäner och har utrymme för förbättring. Syftet med detta projekt är att svara på frågan "Hur kan vi generera bättre preload listor?" och att bidra med förbättrade metoder för att generera preload listor, med det ultimata målet att generera bättre preload listor som Torprojektet kan dra nytta av. Vi vidareutvecklade befintliga verktyg till att använda webbkrypning för att hitta mer användbara domäner, samt implementerade filtrering av värdelösa domäner från preload listorna. Detta projekt visade lovande resultat, då de värdelösa domänerna minskade med i genomsnitt ungefär 57% och mer användbara domäner hittades.

With the growth of the internet and exhaustion of IPv4 addresses, the allocation of IP addresses and routing between autonomous systems is an important factor on what paths are taken on the internet. Paths to different destinations are impacted by different neighbouring autonomous systems and their relations with eachother are important in order to find an optimal route from source to destination. In this thesis we look at a longitudinal change of IP observed on the internet that is owned by large organizations. To achieve this we build tools for extracting and parsing data from a dataset from iPlane where we then compare this to the largest domains and cloud providers. From our results we conclude that large domains and cloud providers are found more often as time has passed and they seem to not peer with eachother. We also find that the routing policies within different autonomous systems varies.

This research presents a hybrid algorithm that combines List Scheduling (LS) with a Genetic Algorithm (GA) for constructing non-preemptive schedules for soft real-time parallel applications represented as directed acyclic graphs (DAGs). The execution time requirements of the applications' tasks are assumed to be stochastic and are represented as probability distribution functions. The performance in terms of schedule lengths for three different genetic representation schemes are evaluated and compared for a number of different DAGs. The approaches presented in this research produce shorter schedules than HLFET, a popular LS approach for all of the sample problems. Of the three genetic representation schemes investigated, PosCT, the technique that allows the GA to learn which tasks to delay in order to allow other tasks to complete produced the shortest schedules for a majority of the sample DAGs.

Verzerrungen durch sozial erwünschtes Antwortverhalten bei heiklen Fragen stellen ein großes Problem der empirischen Sozialforschung dar. Dies führt bei Umfragen zu Item- und Unit-Nonresponse sowie zu unehrlichen Antworten. Diese Verzerrung durch soziale Erwünschtheit (Social Desirability Bias, SD-Bias) führt oftmals dazu, dass Fragen zu sozial erwünschtem Verhalten oder Meinungen (z. B. Wahlteilnahme, Blutspenden oder soziales Engagement) überschätzt und zu sozial unerwünschtem Verhalten oder Einstellungen (z. B. Drogenkonsum, Rassismus oder Diebstahl) unterschätzt werden. In dieser Dissertation wird anhand eines Survey-Experiments überprüft ob die Item Count Technique (ICT) und ihr ähnliche Count-Techniken (z.B. Person Count Techique, Item Sum Technique etc.) bessere Ergebnisse bei heiklen Fragen erzielen als direktes Fragen. Dies wurde mit einem experimentellen Design anhand eines Online Access Panels (n = 3044) überprüft. Dabei wurden die gleichen heiklen Fragen in der Experimentalgruppe mit Count-Techniken gestellt (n =2527) und in der Kontrollgruppe (n = 517) direkt gestellt. Insgesamt konnten Count-Techniken keine besseren Ergebnisse erzielen als direktes Fragen.:1 Problemstellung 1.1 Auswirkungen heikler Fragen 2 Dimensionen heikler Fragen 2.1 Soziale Erwünschtheit 2.2 Eingriff in die Privatsphäre 2.3 Gefahr der Aufdeckung 2.4 Psychologische Kosten 2.5 Definition 3 Soziale Erwünschtheit 3.1 Social-Desirability-Neigung 3.1.1 Definition und Konstrukt 3.2 Messung sozialer Erwünschtheit 3.2.1 Die Crowne-Marlowe-Skala 3.2.2 Das Balanced Inventory of Desirable Responding (BIDR) 3.3 Social Desirability Belief und Trait Desirability 3.3.1 Messung des SD-Beliefs 3.3.2 Trait Desirability 4 Handlungstheorie und Befragtenverhalten 4.1 SEU-Theorie und heikle Fragen 4.2 Das Modell der Frame-Selection und heikle Fragen 5 Externe Effekte und heikle Fragen 5.1 Interviewereffekte und heikle Fragen 5.2 Bystander-Effekte und soziale Erwünschtheit 5.3 Modus-Effekte und heikle Fragen 6 Klassische Lösungen für heikle Fragen 6.1 Bogus Pipeline 6.2 Sealed Envelope 6.3 Forgiving Wording 7 Die Randomized Response Technique 7.1 Entstehung und Grundprinzip nach Warner (1965) 7.2 Die „Unrelated Question“ Technique 7.3 Die „Forced Response“ Technique 7.4 Takahasi’s Technique 7.5 Die „Two Step Procedure“ 7.6 Kuks Card Method 7.7 Multivariate Analyse mit der RRT 7.8 Empirische Tests der RRT 7.9 Handlungstheorie und RRT 7.10 Allgemeine Probleme der RRT 8 Das Crosswise-Model 8.1 Entstehung und Grundprinzip 8.2 Empirischer Test des Crosswise-Models mit Meta-Analyse 9 Die Item Count Technique 9.1 Probleme und Design der ICT-Fragen 9.2 Handlungstheorie und ICT 9.3 Kurze Geschichte der ICT 9.4 Das Double List Design 9.5 Die Person Count Technique 9.6 Die Fixed und Variable Person Count Technique 9.7 Die Item Sum Technique 9.8 Die Person Sum Technique 9.9 Multivariate Analyse von ICT-Daten 9.10 Bisherige empirische Tests der Item Count Technique mit Meta-Studie 10 Empirischer Test der Count-Techniken 10.1 Hypothesen 10.2 Grundlegendes Design 10.3 Datenerhebung 10.4 Operationalisierung 10.4.1 SD-Neigung (BIDR16) 10.4.2 SD-Belief und Trait Desirability 10.4.3 Modell der Frame Selection 10.5 Grundlegende Annahmen und Probleme der Hypothesenprüfung 10.6 Prüfung der Item Count Technique 10.7 Prüfung der Person Count Technique 10.8 Prüfung der Fixed Person Count Technique 10.9 Prüfung der Variable Person Count Technique 10.10Prüfung der Item Sum Technique 10.11Prüfung der Person Sum Technique 10.12Prüfung der Count-Techniken insgesamt 11 Prüfung der Theorien des Befragtenverhaltens 11.1 Test der SEU-Theorie 11.2 Test des Modells der Frame Selection 11.3 Prüfung der handlungstheoretischen Betrachtung der Count-Techniken 12 Fazit 13 Anhang 13.1 Deskriptive Statistiken 13.1.1 BIDR16 13.1.2 SD-Belief 13.1.3 Umfrageeinstellung Literatur

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