New Challenges for an Open Society

The movement of peoples over the past few decades had a considerable impact. Immigration today is profoundly different from the displacement of populations in the past centuries: it really is the most visible aspect of globalization, which gives many people a sense that their familiar world is vanishing. The subject of immigration and integration – and therefore of citizenship – creates uncertainty because it affects so many areas of life: education systems, welfare provision, constitutional rights such as freedom of expression. Natives and newcomers often seem far apart, and there is a diffuse inability of receiving societies to find ways of dealing with immigrants. In the latter end of the article, the author examines how the conflicts surrounding migration can bring about a renewal of society as a whole, leading the way for an open society

Oculomotoric Biometric Identification under the Influence of Alcohol and Fatigue

Patterns of micro- and macro-movements of the eyes are highly individual and can serve as a biometric characteristic. It is also known that both alcohol inebriation and fatigue can reduce saccadic velocity and accuracy. This prompts the question of whether changes of gaze patterns caused by alcohol consumption and fatigue impact the accuracy of oculomotoric biometric identification. We collect an eye tracking data set from 66 participants in sober, fatigued and alcohol-intoxicated states. We find that after enrollment in a rested and sober state, identity verification based on a deep neural embedding of gaze sequences is significantly less accurate when probe sequences are taken in either an inebriated or a fatigued state. Moreover, we find that fatigue and intoxication appear to randomize gaze patterns: when the model is fine-tuned for invariance with respect to inebriation and fatigue, and even when it is trained exclusively on inebriated training person, the model still performs significantly better for sober than for sleep-deprived or intoxicated subjects

Pre-Trained Language Models Augmented with Synthetic Scanpaths for Natural Language Understanding

Human gaze data offer cognitive information that reflects natural language comprehension. Indeed, augmenting language models with human scanpaths has proven beneficial for a range of NLP tasks, including language understanding. However, the applicability of this approach is hampered because the abundance of text corpora is contrasted by a scarcity of gaze data. Although models for the generation of human-like scanpaths during reading have been developed, the potential of synthetic gaze data across NLP tasks remains largely unexplored. We develop a model that integrates synthetic scanpath generation with a scanpath-augmented language model, eliminating the need for human gaze data. Since the model's error gradient can be propagated throughout all parts of the model, the scanpath generator can be fine-tuned to downstream tasks. We find that the proposed model not only outperforms the underlying language model, but achieves a performance that is comparable to a language model augmented with real human gaze data. Our code is publicly available.Comment: Pre-print for EMNLP 202

Pre-Trained Language Models Augmented with Synthetic Scanpaths for Natural Language Understanding

Human gaze data offer cognitive information that reflects natural language comprehension. Indeed, augmenting language models with human scanpaths has proven beneficial for a range of NLP tasks, including language understanding. However, the applicability of this approach is hampered because the abundance of text corpora is contrasted by a scarcity of gaze data. Although models for the generation of human-like scanpaths during reading have been developed, the potential of synthetic gaze data across NLP tasks remains largely unexplored. We develop a model that integrates synthetic scanpath generation with a scanpath-augmented language model, eliminating the need for human gaze data. Since the model’s error gradient can be propagated throughout all parts of the model, the scanpath generator can be fine-tuned to downstream tasks. We find that the proposed model not only outperforms the underlying language model, but achieves a performance that is comparable to a language model augmented with real human gaze data. Our code is publicly available

Fairness in Oculomotoric Biometric Identification

Gaze patterns are known to be highly individual, and therefore eye movements can serve as a biometric characteristic. We explore aspects of the fairness of biometric identification based on gaze patterns. We find that while oculomotoric identification does not favor any particular gender and does not significantly favor by age range, it is unfair with respect to ethnicity. Moreover, fairness concerning ethnicity cannot be achieved by balancing the training data for the best-performing model

Inadequate conflit of interest policies at most French teaching hospitals : a survey and web analysis

Background. There are 32 teaching hospitals in France, including 30 University hospitals and 2 Regional teachinghospitals. Teaching hospitals have three roles: health care provision, training of healthcare professionals, and medicalresearch. These roles lead to frequent interactions with pharmaceutical and medical device companies, inevitably raisingmajor risks of conflicts of interests. Therefore, policies to manage conflict of interests (COI) are crucial. This study aimsto examine COI policies in French teaching hospitals..Methods. All French teaching hospitals (n=32) were included in this study. All hospitals websites were screened forinstitutional COI policies and curriculum on COI, using standardized keyword searches. More data were collected througha questionnaire addressed to each chief executive officer (CEO) of the teaching hospital. We used predefined criteria (n=20) inspired by similar surveys on COI policies in French, US and Canadian medical schools, with some additions toreflect the local hospital context. A global score for each hospital, ranging from 0 to 58 (higher scores denoting strongerpolicies) was calculated by summing points obtained for each criterion.Results. Three out of 32 (9%) CEOs replied to the questionnaire. All 32 hospitals had websites; 16 hospitals listed policiesor regulations on their websites or provided them on request. In December 2017, among the 32 hospitals, we foundthat 17 (53.1%) had rules and regulations for some items only, 4 (12.5%) have considered implementing a policy, two ofwhich (6.3%) have begun implementation. and 15 (46.9%) had no evidence of COI policies and a null score. The maximumglobal score was 24 out of 58, with a mean of 3.50 ± 5.72.Conclusion. This is the first systematic assessment of COI policies in teaching hospitals in France. Such policies areneeded to protect patients, clinicians and students from undue commercial influence. Despite public and political pressurefor better management of COI since France’s benfluorex (Mediator) scandal of 2010, few teaching hospitals haveimplemented comprehensive and protective policies. We hope that periodic ranking of hospitals will contribute to raiseawareness of the importance of COI policy and speed introduction

Eyettention: An Attention-based Dual-Sequence Model for Predicting Human Scanpaths during Reading

Eye movements during reading offer insights into both the reader's cognitive processes and the characteristics of the text that is being read. Hence, the analysis of scanpaths in reading have attracted increasing attention across fields, ranging from cognitive science over linguistics to computer science. In particular, eye-tracking-while-reading data has been argued to bear the potential to make machine-learning-based language models exhibit a more human-like linguistic behavior. However, one of the main challenges in modeling human scanpaths in reading is their dual-sequence nature: the words are ordered following the grammatical rules of the language, whereas the fixations are chronologically ordered. As humans do not strictly read from left-to-right, but rather skip or refixate words and regress to previous words, the alignment of the linguistic and the temporal sequence is non-trivial. In this paper, we develop Eyettention, the first dual-sequence model that simultaneously processes the sequence of words and the chronological sequence of fixations. The alignment of the two sequences is achieved by a cross-sequence attention mechanism. We show that Eyettention outperforms state-of-the-art models in predicting scanpaths. We provide an extensive within- and across-data set evaluation on different languages. An ablation study and qualitative analysis support an in-depth understanding of the model's behavior