Evaluating the usability and security of a video CAPTCHA

A CAPTCHA is a variation of the Turing test, in which a challenge is used to distinguish humans from computers (`bots\u27) on the internet. They are commonly used to prevent the abuse of online services. CAPTCHAs discriminate using hard articial intelligence problems: the most common type requires a user to transcribe distorted characters displayed within a noisy image. Unfortunately, many users and them frustrating and break rates as high as 60% have been reported (for Microsoft\u27s Hotmail). We present a new CAPTCHA in which users provide three words (`tags\u27) that describe a video. A challenge is passed if a user\u27s tag belongs to a set of automatically generated ground-truth tags. In an experiment, we were able to increase human pass rates for our video CAPTCHAs from 69.7% to 90.2% (184 participants over 20 videos). Under the same conditions, the pass rate for an attack submitting the three most frequent tags (estimated over 86,368 videos) remained nearly constant (5% over the 20 videos, roughly 12.9% over a separate sample of 5146 videos). Challenge videos were taken from YouTube.com. For each video, 90 tags were added from related videos to the ground-truth set; security was maintained by pruning all tags with a frequency 0.6%. Tag stemming and approximate matching were also used to increase human pass rates. Only 20.1% of participants preferred text-based CAPTCHAs, while 58.2% preferred our video-based alternative. Finally, we demonstrate how our technique for extending the ground truth tags allows for different usability/security trade-offs, and discuss how it can be applied to other types of CAPTCHAs

Interactive Programming by Example

As of today, programming has never been so accessible. Yet, it remains a challenge for end-users: students, non-technical employees, experts in their domains outside of computer science, and so on. With its forecast potential for solving problems by only observing inputs and outputs, programming-by-example was supposed to alleviate complex tasks requiring programming for end-users. The initial ideas of macro-based editors paved the way to subsequent practical solutions, such as spreadsheet transformations from examples. Finding the right program is the core of the programming-by-example systems. However, users find it difficult to trust such generated programs. In this thesis, we contribute to proving that some forms of interaction alleviate, by having users provide examples, the problem of finding correct and reliable programs. We first report on two experiments that enable us to conjecture what kind of interaction brings benefits to programming-by-example. First, we present a new kind of game engine, Pong Designer. In this game engine, by using their finger, users program rules on the fly, by modifying the game state. We analyze its potential, and its eventual downsides that have probably prevented its wide adoption. Second, we present StriSynth, an interactive command-line tool that uses programming-by-example to transform string and collections. The resulting programs can also rename or otherwise manage files. We obtained the result that confirms that many users preferred StriSynth over usual programming languages, but would appreciate to have both. We then report on two new exciting experiments with verified results, using two forms of interaction truly benefiting programming-by-example. Third, on top of a programmingby- example-based engine for extracting structured data out of text files, in this thesis we study two interaction models implemented in a tool named FlashProg: a view of the program with notification about ambiguities, and the asking of clarification questions. In this thesis, we prove that these two interaction models enable users to perform tasks with less errors and to be more confident with the results. Last, for learning recursive tree-to-string functions (e.g., pretty-printers), in this thesis we prove that questioning breaks down the learning complexity from a cubic to a linear number of questions, in practice making programming-by-example even more accessible than regular programming. The implementation, named Prosy, could be easily added to integrated development environments

The role of background knowledge in reading comprehension of subject-specific texts

This thesis investigates the impact of background knowledge in L2 reading comprehension of subject-specific texts, in particular its interaction with grammar knowledge. It explores how different levels of discipline-related background knowledge, grammar knowledge, and self-reported familiarity affect individual differences in L2 reading comprehension in terms of its outcomes and process. A number of studies made assumptions about readers’ knowledge based on their study disciplines or reports by readers themselves, and thus this study also explores the difference between two operationalizations: tested background knowledge and self-reported familiarity. A mixed-methods approach was used by combining two studies: a testing study and a think-aloud study. Altogether 404 students of the School of Economics and Business, University of Ljubljana, Slovenia took part in the study; 22 in the piloting study and 382 in the main study, out of which 358 were engaged in the testing study and 24 in the think-aloud study. The quantitative and qualitative datasets were obtained from five research instruments: a grammar test, a test of discipline-related background knowledge, a reading comprehension test based on three finance texts, a post-reading questionnaire, and think-aloud verbal protocols. The results of multiple regression revealed that tested background knowledge was a significant medium strength predictor of reading comprehension, slightly stronger than grammar knowledge. In contrast, self-reported familiarity was not found to impact reading comprehension and was not its predictor. This evidence casts doubt over self-reporting as an operationalization of knowledge in L2 reading. Apart from having a facilitative effect on L2 reading comprehension, background knowledge was found to have compensatory and additive roles when interacting with grammar knowledge. The findings showed that readers with higher discipline-related background knowledge could use it to make up for lower grammar knowledge and vice versa, thus suggesting the compensation effect between the two variables. In addition, the results revealed that readers were able to use their background knowledge regardless of their level of grammar knowledge, albeit slightly less at higher levels of grammar knowledge. This finding suggests that the threshold hypothesis could not be supported. Finally, the group of students with both high background knowledge and high grammar knowledge outperformed other groups in reading comprehension, which suggests that the two variables affect reading comprehension in an additive way. The qualitative data from verbal protocols in the think-aloud study and readers’ scores from the testing study were obtained to compare the processing patterns and strategies used by readers with high and low background knowledge. Although both groups were found to use the same types of strategies, they differed in the frequency of their use. While the high background knowledge group used more correct paraphrases, elaboration, inferences, and evaluating, the low knowledge group adopted a more local-level approach by paying more attention to individual words, phrases, and sentences and reporting on various comprehension problems and inability to see the bigger picture. The results suggest differences between the groups with different levels of background knowledge with regard to semantic and pragmatic processing at the local and global level. Analysis of the verbal protocol and post-questionnaire data revealed that specialist vocabulary was the main source of difficulty in L2 reading comprehension of subject-specific texts

LWA 2013. Lernen, Wissen & Adaptivität ; Workshop Proceedings Bamberg, 7.-9. October 2013

LWA Workshop Proceedings: LWA stands for "Lernen, Wissen, Adaption" (Learning, Knowledge, Adaptation). It is the joint forum of four special interest groups of the German Computer Science Society (GI). Following the tradition of the last years, LWA provides a joint forum for experienced and for young researchers, to bring insights to recent trends, technologies and applications, and to promote interaction among the SIGs