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This study explored the relationship between successful guessing and latent ability in IRT models. A new IRT model was developed with a guessing function integrating probability of guessing an item correctly with the examinee's ability and the item parameters. The conventional 3PL IRT model was compared with the new 2PL-Guessing model on parameter estimation using the Monte Carlo method. SAS program was used to implement the data simulation and the maximum likelihood estimation. Compared with the traditional 3PL model, the new model should reflect: a) the maximum probability of guessing should not be more than 0.5, even for the highest ability examinees; b) different ability of examinees should have different probability of successful guessing, because a basic assumption for the new models is that higher ability examinees have a higher probability of successful guessing than lower ability examinees; c) smaller standard error in estimating parameters; and d) faster running time. The results illustrated that the new 2PL-Guessing model was superior to the 3PL model in all four aspects.

When given a multiple-choice test over unfamiliar material, students may score significantly above chance levels. This performance may be explained by prior knowledge of the material or by “test-wiseness,” determining the correct answer by using cues present in the test. Participants answered questions from an introductory psychology test-bank in two formats: a question stem with a single alternative and a traditional four alternative multiple-choice, reporting what sources of information they used to answer each question. For the single-alternative condition, participants had an accuracy of 42.2%, 17.2% higher than the base chance of 25%, with an average accuracy of 40.75% for the multiple-choice condition. Participants who stated they had previously learnt the material showed no significant difference in accuracy than those who stated they had guessed. These findings suggest that tests may have inflated scores which reflect test-wiseness and prior knowledge more than formal learning of the test materials.

This thesis examines some of the currently available programs for password guessing, in terms of designs and strengths. The programs Hashcat, OMEN, PassGAN, PCFG and PRINCE were tested for effectiveness, in a series of experiments similar to real-world attack scenarios. Those programs, as well as the program TarGuess, also had their design examined, in terms of the extent of how they use different important parameters. It was determined that most of the programs use different models to deal with password lists, in order to learn how new, similar, passwords should be generated. Hashcat, PCFG and PRINCE were found to be the most effective programs in the experiments, in terms of number of correct password guessed each second. Finally, a program for automated password guessing based on the results was built and implemented in the cyber range at the Swedish defence research agency.

Tidigare studier har visat att medelvärdet av en grupps gissningar på en faktabaserad fråga ofta blir närmre det rätta svaret än gissningarna var för sig. Det har visat sig att detta även kan appliceras på den enskilda individen genom att denne gör upprepade gissningar på samma fråga. Vidare har man sett att personer med lägre arbetsminnesspann gynnas mer av detta fenomen än personer med högre arbetsminnesspann. Syftet med vår studie är att undersöka huruvida dessa teorier kan appliceras på en grupp vuxna med utvecklingsstörning. I studien deltog 34 personer, 17 personer i försöksgruppen och 17 personer i en köns- och åldersmatchad kontrollgrupp. Ett gissningstest baserat på frågor om omvärldskunskap där svaren skall anges i siffror användes. Vidare utfördes tester för att undersöka mental ålder, språkförståelse samt två domäner av arbetsminne. Resultaten visar att både kontroll- och försöksgruppen gynnas av att ta medelvärdet av sina gissningar. Vi har dock sett att test av denna art är svåra att applicera på en grupp med utvecklingsstörning. Deras svar tycks inte spegla den förståelse de har för frågan och deras gissningar har en stor spridning både inom och över individerna. / Previous studies have shown that the average of guesses made by a group of people, on a question based on general knowledge, often is better than the single guess alone. This has been shown possible to apply on a single individual by producing multiple guesses. One study shows that individuals with low working memory spans benefit more from this phenomenon than individuals with a higher memory span. The aim of our study is to investigate whether these theories can be applied on a group of adults with an intellectual disability. In this study there were 34 participants, 17 subjects in a trial group and 17 subjects in a gender- and chronological age matched control group. A guessing test with questions based on world facts was used. We also conducted tests to investigate mental age, language comprehension and two different domains of working memory. The results show that both our trial group and control group benefits from taking the average of their guesses, although we have seen that this kind of test is difficult to apply on a group with intellectual disability. During the test situation individuals in the trial prove a certain understanding of the numerals, but the given guess does not always reflect this understanding. Also, their guesses are scattered, both within - and across individuals.

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Passwords are common figures in everyone’s everyday life. One person has in average80 accounts for which they are supposed to use different passwords. Remembering allthese passwords is difficult and leads to people reusing, or reusing with slight modification,passwords on many accounts. Studies on memory show that information relating tosomething personal is more easily remembered. This is likely the reason as to why manypeople use passwords relating to either self, relatives, lovers, friends, or pets. Hackers will most often use either brute force or dictionary attacks to crack a password.These techniques can be quite time consuming so using machine learning could bea faster and easier approach. Segmenting someone’s previous passwords into meaningfulunits often reveals personal information about the creator and can thus be used as a basisfor password guessing. This report focuses on evaluating different sizes of the GPT-SW3model, which uses a transformer architecture, on password segmentation. The purposeis to find out if the GPT-SW3 model is suitable to use as a password segmenter and byextension if it can be used for password guessing. As training data, a list of passwords collected from a security breach on a platformcalled RockYou was used. The passwords were segmented by the author to provide themodel with a correct answer to learn from. The evaluation metric, Exact Match, checksif the model’s prediction is the same as that of the author. There were no positive resultswhen training GPT-SW3, most likely because of technical limitations. As the results arerather insufficient, future studies are required to prove or disprove the assumptions thisthesis is based on.

The problem of guessing a random string is studied. It arises in the analysis of the strength of secret-key cryptosystems against guessing attacks. Expected number of guesses, or more generally moments of the number of guesses needed to break the cryptosystem grow exponentially with the length of the string. This thesis studies the rate of exponential growth of these moments using the theory of large deviations. A closer elation between guessing and compression is first established. For systems with large key rates, it is shown that if the source’s sequence of so-called information spectrum random variables satisfies the large deviation property with a certain rate function, then the limiting guessing exponent exists and is a scalar multiple of the Legendre-Fenchel dual of the rate function. This is then used to rederive several prior results. The large deviations approach brings to light the relevance of information spectrum in determining guessing exponents. For systems with key-rate constraints, bounds are derived on the limiting guessing exponents for general sources. The obtained bounds are shown to be tight for stationary memoryless, Markov, and unifilar sources, thus recovering some known results. The bounds are obtained by establishing a close relationship between error exponents and correct decoding exponents for fixed rate source compression on the one hand and exponents for guessing moments on the other.

Online social networks are a natural part of the lives of today’s western youth. Media and information literacy is a central part of research in Library and Information science and until now there has always been more focus on these subjects in a learning context rather than in an every day life context. The aim of this study is to further examine how adolescent boys value and trust the information they receive via Twitter in their personal lives, and how this effects their view of their environment. The study was done with a qualitative method, where we conducted interviews with six boys who were all born in 1997. The results showed that they knew how to react critically to information on Twitter. Although none of the participants valued information on Twitter too seriously, they did feel that it kept them more updated than if they had not been using the social network.