L’ “R-Factor”: un nuovo modo di valutare la ricerca scientifica

As pointed out in Amin e Mabe (2000, p. 1), the journal impact factor (IF) “has moved in recent years from an obscure bibliometric indicator to become the chief quantitative measure of the quality of a journal, its research papers, the researchers who wrote those papers, and even the institution they work in.” However, the use of this index for evaluating individual scientists is dubious and may “skew the course of scientific research” (Monastersky, 2005, p, 1). Moreover the IF is, at most, able to measure only the quality of a very restricted range of research activities: namely, publishing journal articles. In the present work a new indicator of a researcher quality, named the Researcher Impact Factor (RF), is introduced. It is constructed as a function of the number and quality of publications (articles, books and working papers) and of the “other activities” usually associated with being a researcher (attending and/or organizing conferences, being the Editor, Associate Editor or referee for a journal, teaching or supervising at graduate level, coordinating research groups and so on). To show the characteristics of the new index, a numerical example is carried out to rank two hypothetical scientists. The main conclusion is that by replacing the IF with the RF in hiring, tenure decisions and awarding of grants would greatly increase the number of topics investigated and the number and quality of long run projects. The Excel spreadsheet used for the computations is available on demand from the authors.Impact factor, bibliometric indices, research evaluation, researcher impact factor

Does Deep Learning-Based Super-Resolution Help Humans With Face Recognition?

The last decade witnessed a renaissance of machine learning for image processing. Super-resolution (SR) is one of the areas where deep learning techniques have achieved impressive results, with a speci fi c focus on the SR of facial images. Examining and comparing facial images is one of the critical activities in forensic video analysis; a compelling question is thus whether recent SR techniques could help face recognition (FR) made by a human operator, especially in the challenging scenario where very low resolution images are available, which is typical of surveillance recordings. This paper addresses such a question through a simple yet insightful experiment: we used two state- of-the-art deep learning-based SR algorithms to enhance some very low-resolution faces of 30 worldwide celebrities. We then asked a heterogeneous group of more than 130 individuals to recognize them and compared the recognition accuracy against the one achieved by presenting a simple bicubic-interpolated version of the same faces. Results are somehow surprising: despite an undisputed general superiority of SR-enhanced images in terms of visual appearance, SR techniques brought no considerable advantage in overall recognition accuracy

VISION: a video and image dataset for source identification

Abstract Forensic research community keeps proposing new techniques to analyze digital images and videos. However, the performance of proposed tools are usually tested on data that are far from reality in terms of resolution, source device, and processing history. Remarkably, in the latest years, portable devices became the preferred means to capture images and videos, and contents are commonly shared through social media platforms (SMPs, for example, Facebook, YouTube, etc.). These facts pose new challenges to the forensic community: for example, most modern cameras feature digital stabilization, that is proved to severely hinder the performance of video source identification technologies; moreover, the strong re-compression enforced by SMPs during upload threatens the reliability of multimedia forensic tools. On the other hand, portable devices capture both images and videos with the same sensor, opening new forensic opportunities. The goal of this paper is to propose the VISION dataset as a contribution to the development of multimedia forensics. The VISION dataset is currently composed by 34,427 images and 1914 videos, both in the native format and in their social version (Facebook, YouTube, and WhatsApp are considered), from 35 portable devices of 11 major brands. VISION can be exploited as benchmark for the exhaustive evaluation of several image and video forensic tools

Chemists and physicists behaving badly: The shadow side of two elemental discoveries

It is appropriate to recall that 2019 was the year dedicated to the Periodic Table. But when we speak about false elements – in the aftermath of the celebrations marking this year, – we are greeted most warmly, but with some puzzlement, as to how it came to mind to celebrate “Mendeleev’s creature” in such a peculiar way, that is, by commemorating elements that never existed. In the course of many years, we have discovered and collected a great number of discoveries of simple bodies that sooner or later turned out to be detours or false tracks

Chemists and physicists behaving badly: The shadow side of two elemental discoveries

It is appropriate to recall that 2019 was the year dedicated to the Periodic Table. But when we speak about false elements – in the aftermath of the celebrations marking this year, – we are greeted most warmly, but with some puzzlement, as to how it came to mind to celebrate “Mendeleev’s creature” in such a peculiar way, that is, by commemorating elements that never existed. In the course of many years, we have discovered and collected a great number of discoveries of simple bodies that sooner or later turned out to be detours or false tracks