Identifying Human Strategies for Generating Word-Level Adversarial Examples

Adversarial examples in NLP are receiving increasing research attention. One line of investigation is the generation of word-level adversarial examples against fine-tuned Transformer models that preserve naturalness and grammaticality. Previous work found that human- and machine-generated adversarial examples are comparable in their naturalness and grammatical correctness. Most notably, humans were able to generate adversarial examples much more effortlessly than automated attacks. In this paper, we provide a detailed analysis of exactly how humans create these adversarial examples. By exploring the behavioural patterns of human workers during the generation process, we identify statistically significant tendencies based on which words humans prefer to select for adversarial replacement (e.g., word frequencies, word saliencies, sentiment) as well as where and when words are replaced in an input sequence. With our findings, we seek to inspire efforts that harness human strategies for more robust NLP models

Online influence, offline violence: Language Use on YouTube surrounding the 'Unite the Right' rally

The media frequently describes the 2017 Charlottesville ‘Unite the Right’ rally as a turning point for the alt-right and white supremacist movements. Social movement theory suggests that the media attention and public discourse concerning the rally may have engendered changes in social identity performance and visibility of the alt-right, but this has yet to be empirically tested. The presence of the movement on YouTube is of particular interest, as this platform has been referred to as a breeding ground for the alt-right. The current study investigates whether there are differences in language use between 7142 alt-right and progressive YouTube channels, in addition to measuring possible changes as a result of the rally. To do so, we create structural topic models and measure bigram proportions in video transcripts, spanning approximately 2 months before and after the rally. We observe differences in topics between the two groups, with the ‘alternative influencers’, for example, discussing topics related to race and free speech to a larger extent than progressive channels. We also observe structural breakpoints in the use of bigrams at the time of the rally, suggesting there are changes in language use within the two groups as a result of the rally. While most changes relate to mentions of the rally itself, the alternative group also shows an increase in promotion of their YouTube channels. In light of social movement theory, we argue that language use on YouTube shows that the Charlottesville rally indeed triggered changes in social identity performance and visibility of the alt-right

Fixed Point and Aperiodic Tilings

An aperiodic tile set was first constructed by R.Berger while proving the undecidability of the domino problem. It turned out that aperiodic tile sets appear in many topics ranging from logic (the Entscheidungsproblem) to physics (quasicrystals) We present a new construction of an aperiodic tile set that is based on Kleene's fixed-point construction instead of geometric arguments. This construction is similar to J. von Neumann self-reproducing automata; similar ideas were also used by P. Gacs in the context of error-correcting computations. The flexibility of this construction allows us to construct a "robust" aperiodic tile set that does not have periodic (or close to periodic) tilings even if we allow some (sparse enough) tiling errors. This property was not known for any of the existing aperiodic tile sets.Comment: v5: technical revision (positions of figures are shifted

Contrasting Human- and Machine-Generated Word-Level Adversarial Examples for Text Classification

Research shows that natural language processing models are generally considered to be vulnerable to adversarial attacks; but recent work has drawn attention to the issue of validating these adversarial inputs against certain criteria (e.g., the preservation of semantics and grammaticality). Enforcing constraints to uphold such criteria may render attacks unsuccessful, raising the question of whether valid attacks are actually feasible. In this work, we investigate this through the lens of human language ability. We report on crowdsourcing studies in which we task humans with iteratively modifying words in an input text, while receiving immediate model feedback, with the aim of causing a sentiment classification model to misclassify the example. Our findings suggest that humans are capable of generating a substantial amount of adversarial examples using semantics-preserving word substitutions. We analyze how human-generated adversarial examples compare to the recently proposed TEXTFOOLER, GENETIC, BAE and SEMEMEPSO attack algorithms on the dimensions naturalness, preservation of sentiment, grammaticality and substitution rate. Our findings suggest that human-generated adversarial examples are not more able than the best algorithms to generate natural-reading, sentiment-preserving examples, though they do so by being much more computationally efficient

Inguinal hernia – epidemiology, risk factors, treatment methods (literature review)

Inguinal hernias (IH) are widespread in the human population and occur in 27–43 % of men and 3–6 % of women. Many risk factors for IH have been overestimated in the last decade: male gender is considered the leading factor (the ratio between men and women is approximately 1:7), less significant factors are heredity (most significant for women), physical activity (more significant for men), age (peak prevalence of IH occurs at 5 years and 70–80 years), congenital or acquired connective tissue dysplasia, history of prostatectomy, low body mass index.Hernioplasty with the use of synthetic mesh prostheses remains the most popular technique for surgical correction of IH. Performing non-prosthetic hernioplasty is only recommended if mesh prostheses are not available, for example in poor countries. In open hernioplasty using mesh prostheses, different methods are used today: Plug & Patch, Prolene Hernia System, Parietene Progrip, sutureless plastic according to Trabucco, Stoppa, preperitoneal techniques TIPP (trans-inguinal pre-peritoneal), TREPP (transrectus pre-peritoneal), TEP (total extraperitoneal), however, none of them showed significant advantages over the gold standard of open hernioplasty – tensionfree repair according to Liechtenstein.Laparoscopic IH correction is represented by the TAPP (transabdominal preperitoneal) technique, performed through the abdominal cavity, and TEP (total extraperitoneal) – extraperitoneal prosthetic hernioplasty. None of them has a significant advantage in the treatment of IH; therefore, when choosing a treatment method, the surgeon should be guided by the cost of the operation and the level of proficiency in one or another hernioplasty technique