Code Breakers: Beta

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Availability: Usually ships in business days. Blockchain Technology Explained. Mars Rising. Similar Pruducts. Code Breakers: Beta Volume 2. View Product. Code Breakers: Gamma Volume 3. Code Breakers: Delta Volume 4. Buy at Amazon. When we were building Ionic 1 back in , we chose Angular for its powerful component API, wonderful community, and focus on building large web applications. For those using Angular, this means very little will change. Projects can stay at a particular version of Angular, or stay up-to-date with each and every Angular update, and Ionic happily obliges.

The same can be said for using Ionic within Vue, React or no framework at all. Just like with Ionic Angular, our goal is to make it easy to adopt Ionic in the most popular frameworks using their conventional standards. Back when Ionic 2 was released, there was a lot churn and uncertainty with the Angular CLI, build tools, and router.

As such, Ionic had to build its own versions of many of those tools. This means that Angular developers can now use the Angular CLI directly for Ionic apps and stay up-to-date with the awesome progress Angular continues to make. We also wanted to make sure that Ionic Angular used the defacto standard router for the Framework, so again Angular developers can use APIs they are familiar with.

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What will require migration are primarily tooling and routing changes involved with moving to standard Angular utilities for each. In addition to adopting the Angular CLI and Router, our recommended project structure looks exactly like an app started with ng new , aligning Ionic apps with Angular best practices. Most of the changes in the Migration Guide are exactly that: to transition Ionic apps to Angular apps using Angular tooling. This part of the study mainly addresses question a. Here, we are interested in establishing whether an association does exist or not between autistic traits and enrolment in an ethical hacking course in the general population; if such an association exists, it will be also possible to clarify whether some traits e.

A sub-sample of individuals from both groups hackers and non-hackers was also tested with the systemizing quotient SQ questionnaire Baron-Cohen et al. This further addresses question a. Following Baron-Cohen et al. Questions b and c were addressed by introducing behavioral tests in the design. The same subsample was also tested with two behavioral tasks: a prototypical hacking challenge centered around code-breaking skills and an additional security task focused on X-ray image interpretation skills.

While other prototypical hacking tasks e. This made our code-breaking challenge suitable for both groups of participants. Differences in performance were thus expected to emerge at the group level depending on expertise hackers vs.


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Indeed, although the hacking challenge did not require any complex mathematical calculations, it did require individuals to identify, understand, and apply transformation rules from plaintext to cypher-text and back to plaintext. As any exemplar code-breaking test see also Lawson, , for the use of a code breaking test in people with autism , our challenge involved the ability to recursively test hypotheses, and to detect pattern and regularities in apparently random strings of alphanumeric characters to identify structural or conceptual entry points enabling message decryption e.

Based on Baron-Cohen et al. The security X-ray screening task was included as a specificity-control task because it bears no obvious relation to systemizing but it has been previously connected to self-reported attention to detail Rusconi et al. We thus predicted a double dissociation between attention to detail and SQ , whereby the former is more related to performance in the X-ray image interpretation and the latter to performance in the hacking challenge; alternatively, attention to detail could be related to both the X-ray image interpretation task and the hacking challenge, whereas systemizing could be more specifically related to the hacking challenge.

One-hundred and fifty-nine volunteers 79 males and 80 females; in the age range 18—24; 20 in the range: 25—34; 1 in the range: 35—44; 1 in the range 45—54, and 1 in the range 55—64 with no reported learning disabilities completed the AQ. Fifty-six of them 46 males and 10 females were enrolled in an ethical hacking degree course BSc or MSc level or had recently completed it and henceforth will be referred to as hackers. The rest respondents; 33 males and 70 females were enrolled in or had recently completed a different type of degree course mainly BSc or MSc psychology and will be referred to as non-hackers.

Fifty-nine volunteers from this initial sample responded to the subsequent recruitment call for the behavioral study. Data from 56 participants 34 females and 22 males; 51 in the age range 18—24 and 5 in the range 25—34 were eventually included in the analyses, due to incomplete data from three of the original participants.

Of those 56 participants, 13 10 males and 3 females were hackers and 43 12 males and 31 females were non-hackers. The study was approved by the Ethics committee of Abertay University and all participants gave their informed consent online and in writing at the beginning of the testing sessions.

An online version of the original AQ Baron-Cohen et al. The AQ is an agile instrument for quantifying where individuals from the normal population are located on the continuum from autism to normality. It comprises 50 questions divided in five scales 10 questions for each scale measuring social skills, attention switching, communication, imagination and attention to detail.

In order to obtain a general AQ and disassociate heightened attention to detail from other autistic traits, all questions were used in the current study, even though our hypotheses focused on the attention to detail scale. The SQ has good internal consistency, with a Cronbach's alpha coefficient of 0. Compared to the revised version of the questionnaire Wheelwright et al. We dealt with this issue by performing additional analyses to control for possible gender-related confounds.

The hacking challenge comprised a series of code-breaking tasks of increasing complexity.

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Participants were challenged to decrypt six simple messages consisting of single words or a sentence and using codes of increasing complexity, one at a time. They were not allowed to move on to the next level of complexity before having correctly decrypted the easier message. In the tutorial, a list of letters and a list of numbers in decimal notation were provided whereby each number represented a letter in ASCII code. After typing the correct word in a textbox, participants could move onto the second level.

The second and third levels introduced binary and hexadecimal coding. In Mathematics and Computer Science, the binary or base-2 numeral system is a positional system that represents numeric values using only two different symbols: typically 0 and 1. For example, the decimal numeral 27 is in binary notation.

The binary system is the internal operative language of almost all computers and computer-based devices. A binary digit is referred to as a bit and a consecutive series of eight digits is referred to as a byte. Hexadecimal, base, or simply hex, is a numeral system with a base of 16 usually written using the symbols 0—9 and A—F or a—f. For example, the decimal numeral 79 is 4F in hexadecimal.

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Hexadecimal is primarily used in computing to represent a byte, whose possible values can be represented with only two digits in hexadecimal notation. In the Cryptbench level 2 and 3 tutorials, participants were encouraged to scroll along through the hex, ASCII and binary codes, and discover the letters that made up two words encoded in hex. The correct words had to be then typed into a textbox to move on to the next levels.

Next, the concept of encoding by simple addition to ASCII-values and then back into a new text was introduced. To move on to the next level, participants had to transform an encoded word into ASCII, identify the number that was added to the original ASCII code, identify the original word and type it into a textbox. The following level showed how adding to the ASCII-value of a text and then adding a modulo shift to the resulting value, the decryption is made more difficult.

In the final level, the concept of letter frequency analysis was introduced. This was aimed to help identify patterns in an encoded text message consisting of one sentence. The user was thus encouraged to replace encoded letters with potential candidates based on their frequency in text. This would eventually help decrypt the message. The six guided decrypting tasks were followed by three more advanced tasks that had been appositely created for this study. Participants received three differently encrypted messages, along with information about the methods used to encrypt each of them, and a glossary see Appendix A.

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The first hacking challenge involved a combination of Caesar cipher and reversed text. The Caesar cipher is one of the earliest encryption methods and involves replacing plain text letters with letters from the same alphabet but shifted by n places in this case an offset of 2 was applied.

The encoded message was also flipped in reverse, so the participant would then have to flip the message round to read in the correct order. The second challenge was more complex and used a combination of the Caesar cipher offset of 7 , ASCII coding which replaces each letter with a decimal numeral according to the ASCII code and binary coding which translates decimal numerals into binary numerals.

The third challenge was the most complex and used a combination of the Atbash cipher, the Caesar cipher offset of 14 , and the homophonic substitution cipher. The Atbash cipher is a very specific case of substitution cipher where the letters of the alphabet are reversed i. The homophonic substitution cipher involves replacing each letter with a variety of substitutes, the number of potential substitutes being proportional to the frequency of the letter in a given language.

This serves the purpose to undermine the utility of frequency clues to decrypt the message. We used the same stimuli and security X-ray screening task as described in Rusconi et al. In essence, the task consisted of responding as quickly and accurately as possible to whether or not images of X-rayed bags contained a threat e.

In the current study, images were presented and responses recorded with E-Prime professional 2. All participants who had completed the AQ online and provided their contact details were invited to take part in a laboratory testing session. Those respondents, who agreed, were then tested in groups in the ethical hacking laboratories at Abertay University. During testing, each of them had access to a PC with restricted connection to the web i. Participants were not seated next to one another and they were constantly monitored to ensure that no exchange of information occurred during testing.

Including set up, instructions and debriefing, each laboratory session lasted about 1 h. The order of tasks SQ ; hacking challenge; X-ray screening task was counterbalanced between participants.


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During the hacking challenge, participants were instructed to first complete the Cryptbench levels, consisting of six alphanumerical code-breaking tasks. These were then followed by the three advanced code-breaking tasks, to be solved in the same order in which they were presented. Participants were allowed a maximum of 15 min to complete the Cryptbench series, after which the experimenter instructed them to move on to the advanced challenges, with a maximum of 10 min allowed for completion.

These maximum completion times were decided on the basis of previous pilot testing. Each individual task was presented with a series of instructions allowing the participant to be aware of the expectations and the time constraints which came along with each category of tasks.

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Overall completion times and level achieved were recorded. No time limits were set for completing the SQ , which was accessed online from the link provided by the experimenters. No time limits were set for completing the X-ray screening task either. This task was almost invariably completed within 10 min, as participants were encouraged not to take too long a break between the two experimental blocks. At least two experimenters were present and available to answer any questions and to monitor participants throughout the session.

The original binary scoring was used for the AQ data Baron-Cohen et al. Thus, the total AQ score theoretically ranged between 0 and The original scoring method was also used for the SQ data Baron-Cohen et al. Due to the presence of 20 filler items, which are excluded before the scoring procedure, the SQ score theoretically ranged between 0 and Due to violations of the normality assumption see Results Section from all total and subscale scores, questionnaire data were analyzed with non-parametric statistics Field, To test the hypothesis of a relation between AQ , Attention to Detail scores, and interests in hacking, we performed Mann—Whitney U tests between groups hackers vs.

Hacking performance was measured as the number of code-breaking challenges that had been solved within the available time thus represented by an individual score theoretically ranging between 0 and 9 and overall solving time theoretically ranging between 0 and 25 min. Each code-breaking challenge was deemed as solved if the participant had successfully decoded the corresponding message e. To assess whether AQ , Attention to Detail or SQ scores may relate to hacking performance, we calculated Spearman's correlations between questionnaire scores and hacking performance in the sample of 56 participants hackers and non-hackers who completed both the questionnaires and the hacking challenge.

Additional analyses were performed on subsamples of participants to control for possible confounding effects of expertise and gender, and on performance in the X-ray screening task to assess whether the relation that we found between SQ and hacking performance could be explained away by general ability factors. After applying the correction for multiple tests, no significant correlation was detected between the Attention to Detail scale and any of the other subscales. Whereas non-hackers' scores were, on average, similar to the scores reported by Baron-Cohen et al.

It remains to be seen whether this pattern of scores is similar to that obtained by individuals from other STEM disciplines or if it is characteristic of hackers. Descriptive statistics and Mann—Whitney tests for independent samples hackers vs. Along the systemizing dimension, our sample obtained an overall median score of These correlations remained significant even after applying a Bonferroni—Holm correction. No other significant correlations were found. This correlation cannot be accounted for by expertise or gender bias alone see main text.

Dot size is directly proportional to the number of cases placed at the same coordinates. This correlation appeared less robust than the correlation between SQ and with hacking level as it was not significant when controlling for the effects of expertise and gender bias see main text. This may be also due to the majority of participants having used up all the available time to solve the hacking challenge, with considerably reduced variability in the hacking time data compared to the hacking level data.

A Hacking level is shown as a function of Attention to Detail. B Hacking time is shown as a function of Attention to Detail. Because SQ scores co-vary with expertise, we checked if the correlations remained significant after exclusion of the 13 ethical hackers from the sample. In other words, the relation between SQ and hacking level was present and significant even after removing the effect of expertise. This further confirms that differential expertise is not sufficient to account for the reported relation between SQ and hacking performance.

The original version of the SQ is known to be especially affected by a male bias Baron-Cohen et al. To check whether the male bias could act as a confound, we probed again the relation between SQ score and hacking performance after exclusion of the 22 males in our sample. Lastly, we assessed the specificity of the relation between SQ and hacking performance by testing whether high SQ scores were also associated with better performance in a security X-ray screening task.

In contrast, and notwithstanding the smaller sample size compared to Rusconi et al. In this study, we recruited two groups of individuals, hackers and non-hackers, without learning disabilities. We measured the autistic traits of interest via two self-report questionnaires suitable for adults of normal intelligence: the AQ and the SQ Baron-Cohen et al. We measured hacking skills via a tailored hacking challenge that did not require previous expertise but would likely benefit from familiarity and individual predisposition to develop a hacker's mind-set. We also included a control task involving security X-ray image interpretation, which has no obvious relation with the hacker mind-set but had been previously related to piecemeal attention as measured within the AQ e.

Its presence enables us to test the specificity of trait measures known to belong to the same core family of traits Baron-Cohen et al. By taking into account the available evidence on the hacker mind-set and the theoretical proposals relating autistic traits and talent development, we articulated the following predictions: a hackers' AQ , Attention to Detail and SQ scores will be significantly higher than non-hackers'; Attention Switching and scores in other AQ subscales implying mentalizing skills might also be significantly higher in hackers than in non-hackers; b Attention to Detail and SQ scores will be significantly related with hacking skills; c whereas SQ scores will be specifically related with hacking skills i.


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The data showed that a hackers reported significantly higher levels for AQ , Attention to Detail and SQ than non-hackers, whereas they reported the same levels as non-hackers for Attention Switching and other skills involving mentalizing; b SQ scores but not Attention to Detail scores showed a robust relation with performance in the hacking challenge, which cannot be explained by expertise or gender bias only; c SQ scores were not related with performance in the control X-ray screening task, which was instead related with Attention to Detail scores.

Our predictions were thus partly confirmed.



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