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The Streetlight Effect represents an observation bias that occurs when individuals search for something only where it is easiest to look. Despite the significant development of Post-Publication Peer Review (PPPR) in recent years, facilitated in part by platforms such as PubPeer, existing literature has not examined whether PPPR is affected by this type of bias. In other words, if the PPPR mainly concerns publications to which researchers have direct access (eg to analyze image duplications, etc.). In this study, we compare the Open Access (OA) structures of publishers and journals among 51,882 publications commented on PubPeer to those indexed in OpenAlex database (\#156,700,177). Our findings indicate that OA journals are 33% more prevalent in PubPeer than in the global total (52% for the most commented journals). This result can be attributed to disciplinary bias in PubPeer, with overrepresentation of medical and biological research (which exhibits higher levels of openness). However, after normalization, the results reveal that PPPR does not exhibit a Streetlight Effect, as OA publications, within the same discipline, are on average 16% less prevalent in PubPeer than in the global total. These results suggest that the process of scientific self-correction operates independently of publication access status.

This article has a dual objective. Firstly, it aims to investigate whether gender diversity in publications reviewed on Pubpeer has an impact on the (non)retraction of those publications. Secondly, it seeks to analyze the reasons for retractions and examine if there are disparities in retractions based on male-female collaborations. To achieve this, the study utilized a sample of 93,563 publications discussed on Pubpeer spanning the period from 2012 to 2021. The findings reveal that among the reviewed publications, 5% (4,513) were retracted. The concentration index and regression results indicate that publications authored solely by men or led by male authors are 20% to 29% more likely to be retracted compared to those authored solely by women. Regarding the reasons for retractions, the results show that regardless of gender, authors, when working alone, are more prone to engaging in activities such as fake peer review or plagiarism. Women are more concentrated in image manipulation and data errors, while men are more involved in article duplication. Furthermore, the results demonstrate an inverse relationship between the number of authors and retractions, suggesting that a higher number of authors may facilitate better publication control and reduce the temptation for misconduct.

In recent years, Artificial Intelligence (AI) shows a spectacular ability of insertion inside a variety of disciplines which use it for scientific advancements and which sometimes improve it for their conceptual and methodological needs. According to the transverse science framework originally conceived by Shinn and Joerges, AI can be seen as an instrument which is progressively acquiring a universal character through its diffusion across science. In this paper we address empirically one aspect of this diffusion, namely the penetration of AI into a specific field of research. Taking neuroscience as a case study, we conduct a scientometric analysis of the development of AI in this field. We especially study the temporal egocentric citation network around the articles included in this literature, their represented journals and their authors linked together by a temporal collaboration network. We find that AI is driving the constitution of a particular disciplinary ecosystem in neuroscience which is distinct from other subfields, and which is gathering atypical scientific profiles who are coming from neuroscience or outside it. Moreover we observe that this AI community in neuroscience is socially confined in a specific subspace of the neuroscience collaboration network, which also publishes in a small set of dedicated journals that are mostly active in AI research. According to these results, the diffusion of AI in a discipline such as neuroscience didn't really challenge its disciplinary orientations but rather induced the constitution of a dedicated socio-cognitive environment inside this field.

In recent decades the set of knowledge, tools and practices, collectively referred to as "artificial intelligence" (AI), have become a mainstay of scientific research. Artificial intelligence techniques have not only developed enormously within their native areas of development (computer science, mathematics and statistics) but have also spread fast, in terms of application, to multiple areas of science and technology. In this paper we conduct a large scale analysis of artificial intelligence in science. The first question we address is the composition of what is commonly labeled AI, and how the various elements belonging to this domain are linked together. We reconstruct the internal structure of the AI ecosystem through the co-occurrence network of AI terms in publications' abstracts and title, and we propose to distinguish between 15 different specialities of AI, with different temporal patterns. Further, we investigate the spreading of AI outside its native disciplines. We reconstruct the temporal dynamics of the diffusion of AI production in the whole scientific ecosystem and we describe the disciplinary landscape of AI applications. Finally we take a further step analyzing the role of collaborations for the interdisciplinary spreading of AI techniques. While the study of science frequently emphasizes the openness of scientific communities, we show that there are rarely any collaborations between those scholars who primarily develop AI, and those who apply it. Only a small group of researchers is able to gradually establish a bridge between these communities.

One of the major issues of cryptography is the cryptanalysis of cipher algorithms. Cryptanalysis is the study of methods for obtaining the meaning of encrypted information, without access to the secret information that is normally required. Some mechanisms for breaking codes include differential cryptanalysis, advanced statistics and brute-force. Recent works also attempt to use algebraic tools to reduce the cryptanalysis of a block cipher algorithm to the resolution of a system of quadratic equations describing the ciphering structure. In our study, we will also use algebraic tools but in a new way: by using Boolean functions and their properties. A Boolean function is a function from $F_2^n\to F_2$ with $n>1$, characterized by its truth table. The arguments of Boolean functions are binary words of length $n$. Any Boolean function can be represented, uniquely, by its algebraic normal form which is an equation which only contains additions modulo 2 - the XOR function - and multiplications modulo 2 - the AND function. Our aim is to describe the AES algorithm as a set of Boolean functions then calculate their algebraic normal forms by using the M\"obius transforms. After, we use a specific representation for these equations to facilitate their analysis and particularly to try a combinatorial analysis. Through this approach we obtain a new kind of equations system. This equations system is more easily implementable and could open new ways to cryptanalysis., Comment: Submitted to FORmal methods for Security Engineering - ForSE 2017 25 pages

The aim of this paper is to determine the expectations that French-speaking disabled persons have for electronic administrative sites (utility). At the same time, it is a matter of identifying the difficulties of use that the manipulation of these E-services poses concretely for blind people (usability) and of evaluating the psychosocial impacts on the way of life of these people with specific needs. We show that the lack of numerical accessibility is likely to accentuate the social exclusion of which these people are victim by establishing a numerical glass ceiling., Comment: 4 pages visible \`a http://ceur-ws.org/Vol-285