A tool for IoT Firmware Certification

The rapid growth of the Internet of Things (IoT) has created a fragmented ecosystem, with no clear rules for security and reliability. This lack of standardization makes IoT devices vulnerable to attacks. IoT firmware certification can address these security concerns. It empowers consumers to make informed choices by readily identifying secure products. Additionally, it incentivizes developers to prioritize secure coding practices, ultimately promoting transparency and trust within the IoT ecosystem. Several existing IoT device certifications (e.g. Cybersecurity Assurance Program, British Standards Institution, ioXt Alliance) prioritise cybersecurity through risk and vulnerability assessments. This paper proposes a complementary approach. Our tool focuses on identifying firmware functionality by analysing system calls through static analysis. This allows to publicly identify APIs to assess the actual behaviour of a firmware. The analysis culminates in the generation of JSON manifests, which encapsulate the relevant information gathered during the case study. In particular, this analysis verifies whether the actual behaviour is in line with the developer's statements about the device's functionality, contributing to the security and reliability of a device. To evaluate tool's performance, we conducted a benchmarking analysis which has demonstrated efficient handling of binaries written in various languages, even those with large file sizes. Future will be based on refining the API search and syscall collection algorithms, other than incorporating vulnerability analysis to further strengthen the security of an IoT device

Synthetic Data Pretraining for Hyperspectral Image Super-Resolution

Large-scale self-supervised pretraining of deep learning models is known to be critical in several fields, such as language processing, where its has led to significant breakthroughs. Indeed, it is often more impactful than architectural designs. However, the use of self-supervised pretraining lags behind in several domains, such as hyperspectral images, due to data scarcity. This paper addresses the challenge of data scarcity in the development of methods for spatial super-resolution of hyperspectral images (HSI-SR). We show that state-of-the-art HSI-SR methods are severely bottlenecked by the small paired datasets that are publicly available, also leading to unreliable assessment of the architectural merits of the models. We propose to capitalize on the abundance of high resolution (HR) RGB images to develop a self-supervised pretraining approach that significantly improves the quality of HSI-SR models. In particular, we leverage advances in spectral reconstruction methods to create a vast dataset with high spatial resolution and plausible spectra from RGB images, to be used for pretraining HSI-SR methods. Experimental results, conducted across multiple datasets, report large gains for state-of-the-art HSI-SR methods when pretrained according to the proposed procedure, and also highlight the unreliability of ranking methods when training on small datasets

SPI observations of positron annihilation radiation from the 4th galactic quadrant: sky distribution

During its first year in orbit the INTEGRAL observatory performed deep exposures of the Galactic Center region and scanning observations of the Galactic plane. We report on the status of our analysis of the positron annihilation radiation from the 4th Galactic quadrant with the spectrometer SPI, focusing on the sky distribution of the 511 keV line emission. The analysis methods are described; current constraints and limits on the Galactic bulge emission and the bulge-to-disk ratio are presented.Comment: 4 pages, 2 figures, accepted for publication in the proceedings of the 5th INTEGRAL worksho

Detection of Solar Coronal Mass Ejections from Raw Images with Deep Convolutional Neural Networks

Coronal Mass Ejections (CMEs) are massive releases of plasma from the solar corona. When the charged material is ejected towards the Earth, it can cause geomagnetic storms and severely damage electronic equipment and power grids. Early detection of CMEs is therefore crucial for damage containment. In this paper, we study detection of CMEs from sequential images of the solar corona acquired by a satellite. A low-complexity deep neural network is trained to process the raw images, ideally directly on the satellite, in order to provide early alerts

Mathematically-Based Algorithms for Film Digital Restoration

Since its invention, cinema has become an important media of popular culture, becoming part of our historical memory. Unfortunately, films are subject to a fast decay and aging, especially when the conservation conditions are not appropriate. The decay process is irreversible, and the digitalization is becoming the most diffuse and suitable way to conserve and restore films. Classic restoration software involves a significant human intervention and a work of supervision by qualified operators. Indeed, they require a frame-by-frame control and a further phase of manual cleaning. This workflow makes the restoration process expensive in terms of time and money, so that it can\u2019t be afford by small audiovisual archives. The idea of our work is to provide a software which reduce the human intervention and that could be easily used even by small archives, being open source. Here we present, DustRemover, a semi-automatic software for digital film restoration

Synergic combination of the sol-gel method with dip coating for plasmonic devices

Biosensing technologies based on plasmonic nanostructures have recently attracted significant attention due to their small dimensions, low-cost and high sensitivity but are often limited in terms of affinity, selectivity and stability. Consequently, several methods have been employed to functionalize plasmonic surfaces used for detection in order to increase their stability. Herein, a plasmonic surface was modified through a controlled, silica platform, which enables the improvement of the plasmonic-based sensor functionality. The key processing parameters that allow for the fine-tuning of the silica layer thickness on the plasmonic structure were studied. Control of the silica coating thickness was achieved through a combined approach involving sol-gel and dip-coating techniques. The silica films were characterized using spectroscopic ellipsometry, contact angle measurements, atomic force microscopy and dispersive spectroscopy. The effect of the use of silica layers on the optical properties of the plasmonic structures was evaluated. The obtained results show that the silica coating enables surface protection of the plasmonic structures, preserving their stability for an extended time and inducing a suitable reduction of the regeneration time of the chip

In Vitro and In Vivo Antitumor Effect of Anti-CD33 Chimeric Receptor-Expressing EBV-CTL against CD33+ Acute Myeloid Leukemia

Genetic engineering of T cells with chimeric T-cell receptors (CARs) is an attractive strategy to treat malignancies. It extends the range of antigens for adoptive T-cell immunotherapy, and major mechanisms of tumor escape are bypassed. With this strategy we redirected immune responses towards the CD33 antigen to target acute myeloid leukemia. To improve in vivo T-cell persistence, we modified human Epstein Barr Virus-(EBV-) specific cytotoxic T cells with an anti-CD33.CAR. Genetically modified T cells displayed EBV and HLA-unrestricted CD33 bispecificity in vitro. In addition, though showing a myeloablative activity, they did not irreversibly impair the clonogenic potential of normal CD34+ hematopoietic progenitors. Moreover, after intravenous administration into CD33+ human acute myeloid leukemia-bearing NOD-SCID mice, anti-CD33-EBV-specific T cells reached the tumor sites exerting antitumor activity in vivo. In conclusion, targeting CD33 by CAR-modified EBV-specific T cells may provide additional therapeutic benefit to AML patients as compared to conventional chemotherapy or transplantation regimens alone

Protein quantitative trait locus study in obesity during weight-loss identifies a leptin regulator

Although many genetic variants are known for obesity, their function remains largely unknown. Here, in a weight-loss intervention cohort, the authors identify protein quantitative trait loci associated with BMI at baseline and after weight loss and find FAM46A to be a regulator of leptin in adipocytes