Large Language Models (LLMs) are a type of machine learning that handles a wide range of Natural Language Processing (NLP) scenarios. Recently, in December 2022, a company called OpenAI released ChatGPT, a tool that, within a few months, became the most representative example of LLMs, automatically generating unique and coherent text on many topics, summarising and rewriting it, or even translating it to other languages. ChatGPT originated some controversy in academia since students can generate unique text for writing assessments being sometimes extremely difficult to distinguish whether it comes from ChatGPT or a person. In research, some journals specifically banned ChatGPT in scientific papers. However, when used correctly, it becomes a powerful tool to rewrite, for instance, scientific papers and, thus, deliver researchers’ messages in a better way. In this paper, we conduct an empirical study of the impact of ChatGPT in research. We downloaded the abstract of over 45,000 papers from over 300 journals from Dec 2022 and Feb 2023 belonging to different research editorials. We use four of the most known ChatGPT detection tools and conclude that ChatGPT played a role in around 10% of the papers published in every editorial, showing that authors from different fields have rapidly adopted such a tool in their research.

Security and privacy issues in medical wireless body area networks (WBANs) constitute a major unsolved concern because of the challenges posed by the scarcity of resources in WBAN devices and the usability restrictions imposed by the healthcare domain. In this paper, we describe a WBAN architecture based on the well-known publish-subscribe paradigm. We present two protocols for publishing data and sending commands to a sensor that guarantee confidentiality and fine-grained access control. Both protocols are based on a recently proposed ciphertext policy attribute-based encryption (CP-ABE) scheme that is lightweight enough to be embedded into wearable sensors. We show how sensors can implement lattice-based access control (LBAC) policies using this scheme, which are highly appropriate for the eHealth domain. We report experimental results with a prototype implementation demonstrating the suitability of our proposed solution.

MEGARA (Multi-Espectrografo en GTC de Alta Resolucion para Astronomia) is an optical Integral-Field Unit (IFU) and Multi-Object Spectrograph (MOS) designed for the GTC 10.4m telescope in La Palma that is being built by a Consortium led by UCM (Spain) that also includes INAOE (Mexico), IAA-CSIC (Spain), and UPM (Spain). The instrument is currently finishing AIV and will be sent to GTC on November 2016 for its on-sky commissioning on April 2017. The MEGARA IFU fiber bundle (LCB) covers 12.5x11.3 arcsec(2) with a spaxel size of 0.62 arcsec while the MEGARA MOS mode allows observing up to 92 objects in a region of 3.5x3.5 arcmin(2) around the IFU. The IFU and MOS modes of MEGARA will provide identical intermediate-to-high spectral resolutions (R-FWHM similar to 6,000, 12,000 and 18,700, respectively for the low-, mid-and high-resolution Volume Phase Holographic gratings) in the range 3700-9800 angstrom angstrom. An x-y mechanism placed at the pseudo-slit position allows (1) exchanging between the two observing modes and (2) focusing the spectrograph for each VPH setup. The spectrograph is a collimator-camera system that has a total of 11 VPHs simultaneously available (out of the 18 VPHs designed and being built) that are placed in the pupil by means of a wheel and an insertion mechanism. The custom-made cryostat hosts a 4kx4k 15-mu m CCD. The unique characteristics of MEGARA in terms of throughput and versatility and the unsurpassed collecting are of GTC make of this instrument the most efficient tool to date to analyze astrophysical objects at intermediate spectral resolutions. In these proceedings we present a summary of the instrument characteristics and the results from the AIV phase. All subsystems have been successfully integrated and the system-level AIV phase is progressing as expected

The proliferation of wearable and implantable medical devices has given rise to an interest in developing security schemes suitable for these systems and the environment in which they operate. One area that has received much attention lately is the use of (human) biological signals as the basis for biometric authentication, identification and the generation of cryptographic keys. The heart signal (e.g., as recorded in an electrocardiogram) has been used by several researchers in the last few years. Specifically, the so-called Inter-Pulse Intervals (IPIs), which is the time between two consecutive heartbeats, have been repeatedly pointed out as a potentially good source of entropy and are at the core of various recent authentication protocols. In this work, we report the results of a large-scale statistical study to determine whether such an assumption is (or not) upheld. For this, we have analyzed 19 public datasets of heart signals from the Physionet repository, spanning electrocardiograms from 1353 subjects sampled at different frequencies and with lengths that vary between a few minutes and several hours. We believe this is the largest dataset on this topic analyzed in the literature. We have then applied a standard battery of randomness tests to the extracted IPIs. Under the algorithms described in this paper and after analyzing these 19 public ECG datasets, our results raise doubts about the use of IPI values as a good source of randomness for cryptographic purposes. This has repercussions both in the security of some of the protocols proposed up to now and also in the design of future IPI-based schemes.

Browser extensions are small applications executed in the browser context that provide additional capabilities and enrich the user experience while surfing the web. The acceptance of extensions in current browsers is unquestionable. For instance, Chrome’s official extension repository has more than 63,000 extensions, with some of them having more than 10M users. When installed, extensions are pushed into an internal queue within the browser. The order in which each extension executes depends on a number of factors, including their relative installation times. In this paper, we demonstrate how this order can be exploited by an unprivileged malicious extension (i.e., one with no more permissions than those already assigned when accessing web content) to get access to any private information that other extensions have previously introduced. We propose a solution that does not require modifying the core browser engine, since it is implemented as another browser extension. We prove that our approach effectively protects the user against usual attackers (i.e., any other installed extension) as well as against strong attackers having access to the effects of all installed extensions (i.e., knowing who did what). We also prove soundness and robustness of our approach under reasonable assumptions.