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Pharmaceutical patents play an important role by protecting the innovation from copies but also drive researchers to innovate, create new products, and promote disruptive innovations focusing on collective health. The study of patent management usually refers to an exhaustive manual search. This happens, because patent documents are complex with a lot of details regarding the claims and methodology/results explanation of the invention. To mitigate the manual search, we proposed PATopics, a framework specially designed to extract relevant information for Pharmaceutical patents. PATopics is composed of four building blocks that extract textual information from the patents, build relevant topics that are capable of summarizing the patents, correlate these topics with useful patent characteristics and then, summarize the information in a friendly web interface to final users. The general contributions of PATopics are its ability to centralize patents and to manage patents into groups based on their similarities. We extensively analyzed the framework using 4,832 pharmaceutical patents concerning 809 molecules patented by 478 companies. In our analysis, we evaluate the use of the framework considering the demands of three user profiles -- researchers, chemists, and companies. We also designed four real-world use cases to evaluate the framework's applicability. Our analysis showed how practical and helpful PATopics are in the pharmaceutical scenario., Comment: 17 pages, 5 figures, 5 tables

With the proliferation of Internet of Things (IoT) devices, ensuring secure communications has become imperative. Due to their low cost and embedded nature, many of these devices operate with computational and energy constraints, neglecting the potential security vulnerabilities that they may bring. This work-in-progress is focused on designing secure communication among remote servers and embedded IoT devices to balance security robustness and energy efficiency. The proposed approach uses lightweight cryptography, optimizing device performance and security without overburdening their limited resources. Our architecture stands out for integrating Edge servers and a central Name Server, allowing secure and decentralized authentication and efficient connection transitions between different Edge servers. This architecture enhances the scalability of the IoT network and reduces the load on each server, distributing the responsibility for authentication and key management., Comment: 6 pages and 2 figures

Industrial Control Systems (ICS) constitute the backbone of contemporary industrial operations, ranging from modest heating, ventilation, and air conditioning systems to expansive national power grids. Given their pivotal role in critical infrastructure, there has been a concerted effort to enhance security measures and deepen our comprehension of potential cyber threats within this domain. To address these challenges, numerous implementations of Honeypots and Honeynets intended to detect and understand attacks have been employed for ICS. This approach diverges from conventional methods by focusing on making a scalable and reconfigurable honeynet for cyber-physical systems. It will also automatically generate attacks on the honeynet to test and validate it. With the development of a scalable and reconfigurable Honeynet and automatic attack generation tools, it is also expected that the system will serve as a basis for producing datasets for training algorithms for detecting and classifying attacks in cyber-physical honeynets.

We report the identification of 89 new systems containing ultracool dwarf companions to main sequence stars and white dwarfs, using the citizen science project Backyard Worlds: Planet 9 and cross-reference between Gaia and CatWISE2020. Thirty-two of these companions and thirty-three host stars were followed up with spectroscopic observations, with companion spectral types ranging from M7-T9 and host spectral types ranging from G2-M9. These systems exhibit diverse characteristics, from young to old ages, blue to very red spectral morphologies, potential membership to known young moving groups, and evidence of spectral binarity in 9 companions. Twenty of the host stars in our sample show evidence for higher order multiplicity, with an additional 11 host stars being resolved binaries themselves. We compare this sample's characteristics with those of the known stellar binary and exoplanet populations, and find our sample begins to fill in the gap between directly imaged exoplanets and stellary binaries on mass ratio-binding energy plots. With this study, we increase the population of ultracool dwarf companions to FGK stars by $\sim$42\%, and more than triple the known population of ultracool dwarf companions with separations larger than 1,000 au, providing excellent targets for future atmospheric retrievals., Comment: 61 pages, 11 figures, 11 tables. Accepted for publication in AJ

In this study, we investigate the role of the non-linear memory effect in gravitational wave (GW) parameter estimation, particularly we explore its capability to break the degeneracy between luminosity distance and inclination angle in binary coalescence events. Motivated by the rapid growth in GW detections and the increasing sensitivity of GW observatories enhancing the precision of cosmological and astrophysical measurements is crucial. We propose leveraging the non-linear memory effect -- a subtle, persistent feature in the GW signal resulting from the cumulative impact of emitted gravitational waves -- as a novel approach to enhance parameter estimation accuracy. Through a comprehensive series of injection studies, encompassing both reduced and full parameter spaces, we evaluate the effectiveness of non-linear memory in various scenarios for aligned-spin systems. Our findings demonstrate the significant potential of non-linear memory in resolving the inclination-distance degeneracy, particularly for events with high signal-to-noise ratios (SNR $>$ 90) for the current generation of detectors or closer than 1 Gpc in the context of future detector sensitivities such as the planned LIGO A$^\sharp$ upgrade. The results also suggest that excluding non-linear memory from parameter estimation could introduce significant systematics in future LIGO A$^\sharp$ detections. This observation will hold even greater weight for next-generation detectors, highlighting the importance of including non-linear memory in GW models for achieving high-accuracy measurements for gravitational wave (GW) astronomy., Comment: 10 pages, 9 figures

Adversarial attacks significantly threaten the robustness of deep neural networks (DNNs). Despite the multiple defensive methods employed, they are nevertheless vulnerable to poison attacks, where attackers meddle with the initial training data. In order to defend DNNs against such adversarial attacks, this work proposes a novel method that combines the defensive distillation mechanism with a denoising autoencoder (DAE). This technique tries to lower the sensitivity of the distilled model to poison attacks by spotting and reconstructing poisonous adversarial inputs in the training data. We added carefully created adversarial samples to the initial training data to assess the proposed method's performance. Our experimental findings demonstrate that our method successfully identified and reconstructed the poisonous inputs while also considering enhancing the DNN's resilience. The proposed approach provides a potent and robust defense mechanism for DNNs in various applications where data poisoning attacks are a concern. Thus, the defensive distillation technique's limitation posed by poisonous adversarial attacks is overcome., Comment: This paper have 4 pages, 3 figures and it is accepted at the Ada User journal

As the Internet of Things (IoT) continues to expand, data security has become increasingly important for ensuring privacy and safety, especially given the sensitive and, sometimes, critical nature of the data handled by IoT devices. There exist hardware-based trusted execution environments used to protect data, but they are not compatible with low-cost devices that lack hardware-assisted security features. The research in this paper presents software-based protection and encryption mechanisms explicitly designed for embedded devices. The proposed architecture is designed to work with low-cost, low-end devices without requiring the usual changes on the underlying hardware. It protects against hardware attacks and supports runtime updates, enabling devices to write data in protected memory. The proposed solution is an alternative data security approach for low-cost IoT devices without compromising performance or functionality. Our work underscores the importance of developing secure and cost-effective solutions for protecting data in the context of IoT., Comment: 4

We propose a hierarchical framework for collaborative intelligent systems. This framework organizes research challenges based on the nature of the collaborative activity and the information that must be shared, with each level building on capabilities provided by lower levels. We review research paradigms at each level, with a description of classical engineering-based approaches and modern alternatives based on machine learning, illustrated with a running example using a hypothetical personal service robot. We discuss cross-cutting issues that occur at all levels, focusing on the problem of communicating and sharing comprehension, the role of explanation and the social nature of collaboration. We conclude with a summary of research challenges and a discussion of the potential for economic and societal impact provided by technologies that enhance human abilities and empower people and society through collaboration with Intelligent Systems.

AEROS is a 3U CubeSat pathfinder toward a future ocean-observing constellation, targeting the Portuguese Atlantic region. AEROS features a miniaturized, high-resolution Hyperspectral Imager (HSI), a 5MP RGB camera, and a Software Defined Radio (SDR). The sensor generated data will be processed and aggregated for end-users in a new web-based Data Analysis Center (DAC). The HSI has 150 spectrally contiguous bands covering visible to near-infrared with 10 nm bandwidth. The HSI collects ocean color data to support studies of oceanographic characteristics known to influence the spatio-temporal distribution and movement behavior of marine organisms. Usage of an SDR expands AEROS's operational and communication range and allows for remote reconfiguration. The SDR receives, demodulates, and retransmits short duration messages, from sources including tagged marine organisms, autonomous vehicles, subsurface floats, and buoys. The future DAC will collect, store, process, and analyze acquired data, taking advantage of its ability to disseminate data across the stakeholders and the scientific network. Correlation of animal-borne Argos platform locations and oceanographic data will advance fisheries management, ecosystem-based management, monitoring of marine protected areas, and bio-oceanographic research in the face of a rapidly changing environment. For example, correlation of oceanographic data collected by the HSI, geolocated with supplementary images from the RGB camera and fish locations, will provide researchers with near real-time estimates of essential oceanographic variables within areas selected by species of interest., Comment: 13 pages, 16 figures, Manuscript presented at the 73rd International Astronautical Congress, IAC 2022, Paris, France, 18 - 22 September 2022

MAGAL lays the foundations for a future constellation of small satellites carrying radar altimeters aiming to improve the understanding of ocean circulation variability at local, regional, and global scales. All necessary tools will be developed, including a new small, low-power altimeter payload and a miniaturized satellite platform, grounded on the Space 4.0 industry, to be manufactured inseries, minimizing production, operational and launch costs. To implement a collaborative constellation, and better tackle the gaps of large radar altimeter programmes, MAGAL will use a Data Analysis Centre, based on cloud services, for storage and process of data, based on known and improved algorithms, including overlay of layers from multiple sources (e.g. meteorology and opensource data). As a constellation of six satellites, MAGAL increases the density of sea surface topography measurements, enabling more data for altimetry products, when used in synergy with other missions, in coastal areas and over mesoscale features. This results in scientific and commercial information aggregated into a single platform, displayed in various graphical interfaces, allowing overlaid correlations. MAGAL is aligned with the insights from the EU agenda for sustainable development, adding value, alongside the underlying technology development, bringing together the sea's economy and its sustainable growth., Comment: 15 pages, 7 figures, Manuscript presented at the 4S Symposium 2022, Vilamoura, Portugal, 16 - 20 May 2022

AEROS aims to develop a nanosatellite as a precursor of a future system of systems, which will include assets and capabilities of both new and existing platforms operating in the Ocean and Space, equipped with state-of-the-art sensors and technologies, all connected through a communication network linked to a data gathering, processing and dissemination system. This constellation leverages scientific and economic synergies emerging from New Space and the opportunities in prospecting, monitoring, and valuing the Ocean in a sustainable manner, addressing the demand for improved spatial, temporal, and spectral coverage in areas such as coastal ecosystems management and climate change assessment and mitigation. Currently, novel sensors and systems, including a miniaturized hyperspectral imager and a flexible software-defined communication system, are being developed and integrated into a new versatile satellite structure, supported by an innovative on-board software. Additional sensors, like the LoRaWAN protocol and a wider field of view RGB camera, are under study. To cope with data needs, a Data Analysis Centre, including a cloud-based data and telemetry dashboard and a back-end layer, to receive and process acquired and ingested data, is being implemented to provide tailored-to-use remote sensing products for a wide range of applications for private and institutional stakeholders., Comment: 15 pages, 9 figures, Manuscript presented at the 4S Symposium 2022, Vilamoura, Portugal, 16 - 20 May 2022

The VEDLIoT project targets the development of energy-efficient Deep Learning for distributed AIoT applications. A holistic approach is used to optimize algorithms while also dealing with safety and security challenges. The approach is based on a modular and scalable cognitive IoT hardware platform. Using modular microserver technology enables the user to configure the hardware to satisfy a wide range of applications. VEDLIoT offers a complete design flow for Next-Generation IoT devices required for collaboratively solving complex Deep Learning applications across distributed systems. The methods are tested on various use-cases ranging from Smart Home to Automotive and Industrial IoT appliances. VEDLIoT is an H2020 EU project which started in November 2020. It is currently in an intermediate stage with the first results available., Comment: This publication incorporates results from the VEDLIoT project, which received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 957197

Hierarchical Task Networks (HTN) planners generate plans using a decomposition process with extra domain knowledge to guide search towards a planning task. While domain experts develop HTN descriptions, they may repeatedly describe the same preconditions, or methods that are rarely used or possible to be decomposed. By leveraging a three-stage compiler design we can easily support more language descriptions and preprocessing optimizations that when chained can greatly improve runtime efficiency in such domains. In this paper we evaluate such optimizations with the HyperTensioN HTN planner, used in the HTN IPC 2020., Comment: Preprint version of journal submission

This article introduces "Baby Robot", a robot aiming to improve motor skills of babies and toddlers. Authors developed a car-like toy that moves autonomously using reinforcement learning and computer vision techniques. The robot behaviour is to escape from a target baby that has been previously recognized, or at least detected, while avoiding obstacles, so that the security of the baby is not compromised. A myriad of commercial toys with a similar mobility improvement purpose are into the market; however, there is no one that bets for an intelligent autonomous movement, as they perform simple yet repetitive trajectories in the best of the cases. Two crawling toys -- one in representation of "Baby Robot" -- were tested in a real environment with respect to regular toys in order to check how they improved the toddlers mobility. These real-life experiments were conducted with our proposed robot in a kindergarten, where a group of children interacted with the toys. Significant improvement in the motion skills of participants were detected.

In this paper we present an extensive analysis of the GW190521 gravitational wave event with the current (fourth) generation of phenomenological waveform models for binary black hole coalescences. GW190521 stands out from other events since only a few wave cycles are observable. This leads to a number of challenges, one being that such short signals are prone to not resolve approximate waveform degeneracies, which may result in multi-modal posterior distributions. The family of waveform models we use includes a new fast time-domain model IMRPhenomTPHM, which allows us extensive tests of different priors and robustness with respect to variations in the waveform model, including the content of spherical harmonic modes. We clarify some issues raised in a recent paper [Nitz&Capano], associated with possible support for a high-mass ratio source, but confirm their finding of a multi-modal posterior distribution, albeit with important differences in the statistical significance of the peaks. In particular, we find that the support for both masses being outside the PISN mass-gap, and the support for an intermediate mass ratio binary are drastically reduced with respect to what Nitz&Capano found. We also provide updated probabilities for associating GW190521 to the potential electromagnetic counterpart from ZTF., Comment: 20 pages, 13 figures, 7 tables. Data release at https://zenodo.org/record/4751655

We present IMRPhenomTPHM, a phenomenological model for the gravitational wave signals emitted by the coalescence of quasi-circular precessing binary black holes systems. The model is based on the "twisting up" approximation, which maps non-precessing signals to precessing ones in terms of a time dependent rotation described by three Euler angles, and which has been utilized in several frequency domain waveform models that have become standard tools in gravitational wave data analysis. Our model is however constructed in the time domain, which allows several improvements over the frequency domain models: we do not use the stationary phase approximation, we employ a simple approximation for the precessing Euler angles for the ringdown signal, and we implement a new method for computing the Euler angles through the evolution of the spin dynamics of the system, which is more accurate and also computationally efficient., Comment: 18 pages, 10 figures, 4 tables

We consider the ten confidently detected gravitational-wave signals in the GWTC-1 catalog which are consistent with mergers of binary black hole systems, and perform a thorough parameter estimation re-analysis. This is made possible by using computationally efficient waveform models of the current (fourth) generation of the IMRPhenom family of phenomenological waveform models, which consists of the IMRPhenomX frequency-domain modelsand the IMRPhenomT time-domain models. The analysis is performed with both precessing and non-precessing waveform models with and without subdominant spherical harmonic modes. Results for all events are validated with convergence tests, discussing in particular the events GW170729 and GW151226. For the latter and the other two lowest-mass events, we also compare results between two independent sampling codes, Bilby and LALInference. We find overall consistent results with the original GWTC-1 results, with all Jensen-Shannon divergences between the previous results using IMRPhenomPv2 and our default IMRPhenomXPHM posteriors below 0.045 bits, but we also discuss cases where including subdominant harmonics and/or precession influences the posteriors., Comment: 24 pages, 19 figures, 9 tables matches version accepted by MNRAS

In this work we present an extension of the time domain phenomenological model IMRPhenomT for gravitational wave signals from binary black hole coalescences to include subdominant harmonics, specifically the $(l=2, m=\pm 1)$, $(l=3, m=\pm 3)$, $(l=4, m=\pm 4)$ and $(l=5, m=\pm 5)$ spherical harmonics. We also improve our model for the dominant $(l=2, m=\pm 2)$ mode and discuss mode mixing for the $(l=3, m=\pm 2)$ mode. The model is calibrated to numerical relativity solutions of the full Einstein equations up to mass ratio 18, and to numerical solutions of the Teukolsky equations for higher mass ratios. This work complements the latest generation of traditional frequency domain phenomenological models (IMRPhenomX), and provides new avenues to develop computationally efficient models for gravitational wave signals from generic compact binaries., Comment: 22 pages, 18 figures. https://dcc.ligo.org/P2000524