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Cross-site scripting (XSS) poses a significant threat to web application security. While Deep Learning (DL) has shown remarkable success in detecting XSS attacks, it remains vulnerable to adversarial attacks due to the discontinuous nature of its input-output mapping. These adversarial attacks employ mutation-based strategies for different components of XSS attack vectors, allowing adversarial agents to iteratively select mutations to evade detection. Our work replicates a state-of-the-art XSS adversarial attack, highlighting threats to validity in the reference work and extending it toward a more effective evaluation strategy. Moreover, we introduce an XSS Oracle to mitigate these threats. The experimental results show that our approach achieves an escape rate above 96% when the threats to validity of the replicated technique are addressed.

Finding proper collective variables for complex systems and processes is one of the most challenging tasks in simulations, which limits the interpretation of experimental and simulated data and the application of enhanced sampling techniques. Here, we propose a machine learning approach able to distill few, physically relevant variables by associating instantaneous configurations of the system to their corresponding inherent structures as defined in liquids theory. We apply this approach to the challenging case of structural transitions in nanoclusters, managing to characterize and explore the structural complexity of an experimentally relevant system constituted by 147 gold atoms. Our inherent-structure variables are shown to be effective at computing complex free-energy landscapes, transition rates, and at describing non-equilibrium melting and freezing processes. The effectiveness of this machine learning strategy guided by the generally-applicable concept of inherent structures shows promise to devise collective variables for a vast range of systems, including liquids, glasses, and proteins.

In this article, we present an efficient multi-robot active SLAM framework that involves a frontier-sharing method for maximum exploration of an unknown environment. It encourages the robots to spread into the environment while weighting the goal frontiers with the pose graph SLAM uncertainly and path entropy. Our approach works on a limited number of frontier points and weights the goal frontiers with a utility function that encapsulates both the SLAM and map uncertainties, thus providing an efficient and not computationally expensive solution. Our approach has been tested on publicly available simulation environments and on real robots. An accumulative 31% more coverage than similar state-of-the-art approaches has been obtained, proving the capability of our approach for efficient environment exploration., Comment: 14 pages, 9 figures

In autonomous robotics, a critical challenge lies in developing robust solutions for Active Collaborative SLAM, wherein multiple robots collaboratively explore and map an unknown environment while intelligently coordinating their movements and sensor data acquisitions. In this article, we present an efficient centralized frontier sharing approach that maximizes exploration by taking into account information gain in the merged map, distance, and reward computation among frontier candidates and encourages the spread of agents into the environment. Eventually, our method efficiently spreads the robots for maximum exploration while keeping SLAM uncertainty low. Additionally, we also present two coordination approaches, synchronous and asynchronous to prioritize robot goal assignments by the central server. The proposed method is implemented in ROS and evaluated through simulation and experiments on publicly available datasets and similar methods, rendering promising results., Comment: 7 pages, 11 figures 3 Tables

A general method to obtain a representation of the structural landscape of nanoparticles in terms of a limited number of variables is proposed. The method is applied to a large dataset of parallel tempering molecular dynamics simulations of gold clusters of 90 and 147 atoms, silver clusters of 147 atoms, and copper clusters of 147 atoms, covering a plethora of structures and temperatures. The method leverages convolutional neural networks to learn the radial distribution functions of the nanoclusters and to distill a low-dimensional chart of the structural landscape. This strategy is found to give rise to a physically meaningful and differentiable mapping of the atom positions to a low-dimensional manifold, in which the main structural motifs are clearly discriminated and meaningfully ordered. Furthermore, unsupervised clustering on the low-dimensional data proved effective at further splitting the motifs into structural subfamilies characterized by very fine and physically relevant differences, such as the presence of specific punctual or planar defects or of atoms with particular coordination features. Owing to these peculiarities, the chart also enabled tracking of the complex structural evolution in a reactive trajectory. In addition to visualization and analysis of complex structural landscapes, the presented approach offers a general, low-dimensional set of differentiable variables which has the potential to be used for exploration and enhanced sampling purposes., Comment: 28 pages, 13 figures

The scientific community has witnessed an exponential increase in the applications of graphene and graphene-based materials in a wide range of fields. For what concerns neuroscience, the interest raised by these materials is two-fold. On one side, nanosheets made of graphene or graphene derivatives (graphene oxide, or its reduced form) can be used as carriers for drug delivery. Here, an important aspect is to evaluate their toxicity, which strongly depends on flake composition, chemical functionalization and dimensions. On the other side, graphene can be exploited as a substrate for tissue engineering. In this case, conductivity is probably the most relevant amongst the various properties of the different graphene materials, as it may allow to instruct and interrogate neural networks, as well as to drive neural growth and differentiation. In this review, we try to give a comprehensive view of the accomplishments and new challenges of the field, as well as which in our view are the most exciting directions to take in the immediate future. These include the need to engineer multifunctional nanoparticles able to cross the blood-brain-barrier to reach neural cells, and to achieve on-demand delivery of specific drugs. We describe the state-of-the-art in the use of graphene materials to engineer three-dimensional scaffolds to drive neuronal growth and regeneration in vivo, and the possibility of using graphene as a component of hybrid composites/multi-layer organic electronics devices. Last but not least, we address the need of an accurate theoretical modeling of the interface between graphene and biological material, by modeling the interaction of graphene with proteins and cell membranes at the nanoscale, and describing the physical mechanism(s) of charge transfer by which the various graphene materials can influence the excitability and physiology of neural cells., Comment: Invited Review paper, 42 pages, 6 figures. This document is the unedited Author's version of a Submitted Work that was subsequently accepted for publication in Frontiers in Systems Neuroscience. To access the final edited and published work see https://www.frontiersin.org/articles/10.3389/fnsys.2018.00012/full

In this letter we report the preliminary validation of a low-cost paradigm for photovoltaic power generation that utilizes a prismatic Fresnel-like lens to simultaneously concentrate and separate sunlight into continuous laterally spaced spectral bands, which are then fed into spectrally matched single-junction photovoltaic cells. A prismatic lens was designed using geometric optics and the dispersive properties of the employed material, and its performance was simulated with a ray- tracing software. After device optimization, it was fabricated by injection molding, suitable for large-scale mass production. We report an average optical transmittance of ~ 90% over the VNIR range with spectral separation in excellent agreement with our simulations. Finally, two prototype systems were tested: one with GaAsP and c-Si photovoltaic devices and one with a pair of copper indium gallium selenide based solar cells. The systems demonstrated an increase in peak electrical power output of 51% and 64% respectively under white light illumination. Given the ease of manufacturability of the proposed device, the reported spectral splitting approach provides a cost- effective alternative to multi-junction solar cells for efficient light-to-electricity conversion ready for mass production., Comment: 6 pages, 4 figures

In this paper we present and experimentally validate a low-cost design of a spectral splitting concentrator for the efficient conversion of solar energy. The optical device consists of a dispersive prismatic lens made of polycarbonate designed to simultaneously concentrate the solar light and split it into its spectral components. With respect to our previous implementation, this device concentrates the light along two axes and generates a light pattern compatible with the dimensions of a set of concentrating photovoltaic cells while providing a higher concentration ratio. The mathematical framework and the constructive approach used for the design are presented and the device performance is simulated using a ray-tracing software. We obtain spectral separation in the visible range within a 3x1 cm2 area and a maximum concentration of 210x for a single wavelength. The device is fabricated by injection molding and its performance is experimentally investigated. We measure an optical transmissivity above 90% in the range 400-800 nm and we observe a spectral distribution in good accordance with simulations. Our results demonstrate the feasibility of the device for cost effective high efficiency concentrated photovoltaic systems., Comment: 13 pages, 7 figures

A simple, efficient, and accurate method is proposed to map multi-dimensional free energy landscapes. The method combines the temperature-accelerated molecular dynamics (TAMD) proposed in [Maragliano & Vanden-Eijnden, Chem. Phys. Lett. 426, 168 (2006)] with a variational reconstruction method using radial-basis functions for the representation of the free energy. TAMD is used to rapidly sweep through the important regions of the free energy landscape and compute the gradient of the free energy locally at points in these regions. The variational method is then used to reconstruct the free energy globally from the mean force at these points. The algorithmic aspects of the single-sweep method are explained in detail, and the method is tested on simple examples, compared to metadynamics, and finally used to compute the free energy of the solvated alanine dipeptide in two and four dihedral angles.