Consent To Shoot – Rethinking The Anti-satellite Weapon Versus Space Debris Dilemma

Space debris, whether caused by anti-satellite weapons or from collisions with defunct vehicles, has become a serious threat to the safe and sustainable use of space. Technologies have been proposed to mitigate this problem by actively removing debris (ADR) by capturing and de-orbiting the targets (e.g., rendezvous operations, tethers, or harpoons) or by indirectly affecting the target’s orbit (e.g., using lasers). However, rather sooner than later, deploying ADR technologies against healthy satellites turns the tools for making space safer into anti-satellite weapons, capable of crippling other nations’ infrastructure. In an attempt to resolve the tool-versus-weapon dilemma, we discuss in this paper technical solutions that involve a paradigm shift in the Concept of Operations, but that also have the potential to avoid political implications and many concerns that currently prevent us from solving the space-debris problem. The solutions we advocate require consensus between involved stakeholders for all critical operations of an ADR system. We show it is technologically possible and, in fact, already well understood how to enforce that such operations can only be performed consensually. We sketch a distributed infrastructure, capable of supporting such operations among all stakeholders, enforcing agreement in international cooperation about where and for how long an ADR system gets activated, what targets it follows and where safety zones and objects are. In this way, stakeholders have to validate every piece of information to remove single points of failures, but more importantly to put the required mutual trust on solid and technologically enforced foundations

Stochastic Models for Planning VLE Moodle Environments based on Containers and Virtual Machines

Moodle Virtual Learning Environments (VLEs) represent tools of a pedagogical dimension where the teacher uses various resources to stimulate student learning. Content presented in hypertext, audio or vídeo formats can be adopted as a means to facilitate the learning. These platforms tend to produce high processing rates on servers, large volumes of data on the network and, consequently, degrade performance, increase energy consumption and costs. However, to provide eficiente sharing of computing resources and at the same time minimize financial costs, these VLE platforms typically run on virtualized infrastructures such as Virtual Machines (VM) or containers, which have advantages and disadvantages. Stochastic models, such as stochastic Petri nets (SPNs), can be used in the modeling and evaluation of such environments. Therefore, this work aims to use analytical modeling through SPNs to assess the performance, energy consumption and cost of environments based on containers and VMs. Metrics such as throughput, response time, energy consumption and cost are collected and analyzed. The results revealed that, for example, a cluster with 10 replicas, occupied at their maximum capacity, can generate a 46.54% reduction in energy consumption if containers are used. Additionally, we validate the accuracy of the analytical models by comparing their results with the results obtained in a real infrastructure

Enhancing Autonomous Vehicle Safety through N-version Machine Learning Systems

peer reviewedUnreliable outputs of machine learning (ML) models are a significant concern, particularly for safety-critical applications such as autonomous driving. ML models are susceptible to out-of-distribution samples, distribution shifts, hardware transient faults, and even malicious attacks. To address the concerns, the N-version ML system gives a general solution to enhance the reliability of ML system outputs by employing diversification on ML models and their inputs. However, the existing studies of N-version ML systems mainly focused on classification errors and did not consider their impacts in a practical application scenario. In this paper, we investigate the applicability of the N-version ML approach in an autonomous vehicle (AV) scenario within the AV simulator CARLA. We deploy two-version and three-version perception systems in an AV implemented in CARLA, using healthy ML models and compromised ML models, which are generated using fault-injection techniques and analyze the behavior of the AV in the simulator. Our findings reveal the critical impacts of compromised models on AV collision rates and show the potential of three-version perception systems in mitigating the risk. Our three-version perception system improves driving safety by tolerating one compromised model and delaying collisions when having at least one healthy model

ADESÃO AO TRATAMENTO NA CLÍNICA NEUROPSIQUIÁTRICA: UMA REVISÃO SISTEMÁTICA

Adherence to treatment in psychiatric outpatient clinics is a vital element of mental health care. Although significant challenges are faced, it is possible to improve adherence through educational strategies, support and collaboration between patients and healthcare professionals. By recognizing the importance of this aspect and working to overcome obstacles, we can improve outcomes for patients and promote more robust and resilient mental health in our communities. This study aimed to investigate the main factors influencing adherence to treatment in psychiatric outpatient clinics. To this end, a systematic literature review was conducted, selecting scientific articles published between 2019 and 2024, available in the Scielo, Medline and Lilacs databases. After an in-depth analysis and discussion of the results, it was concluded that adherence to treatment in psychiatric outpatient clinics is influenced by a variety of factors, including continuity in therapeutic follow-up, patients' mentalization capacity, the model of care and sociodemographic and clinical influences, highlighting the need for personalized and integrated approaches to improve care in these settings.A adesão ao tratamento em ambulatórios psiquiátricos é um elemento vital do cuidado em saúde mental. Embora sejam enfrentados desafios significativos, é possível melhorar a adesão por meio de estratégias educacionais, de suporte e de colaboração entre pacientes e profissionais de saúde. Ao reconhecer a importância desse aspecto e trabalhar para superar os obstáculos, pode-se melhorar os resultados para os pacientes e promover uma saúde mental mais robusta e resiliente em nossas comunidades. Este estudo teve como objetivo investigar os principais fatores que influenciam a adesão ao tratamento nos ambulatórios psiquiátricos. Para isso, foi conduzida uma revisão sistemática da literatura, selecionando artigos científicos publicados entre 2019 e 2024, disponíveis nas bases de dados Scielo, Medline e Lilacs. Após uma análise aprofundada e discussão dos resultados, chegou-se à conclusão de que a adesão ao tratamento nos ambulatórios psiquiátricos é influenciada por uma variedade de fatores, incluindo a continuidade no acompanhamento terapêutico, a capacidade de mentalização dos pacientes, o modelo de atendimento e as influências sociodemográficas e clínicas, destacando a necessidade de abordagens personalizadas e integradas para melhorar o cuidado nesses ambientes

ATLANTIC EPIPHYTES: a data set of vascular and non-vascular epiphyte plants and lichens from the Atlantic Forest

Epiphytes are hyper-diverse and one of the frequently undervalued life forms in plant surveys and biodiversity inventories. Epiphytes of the Atlantic Forest, one of the most endangered ecosystems in the world, have high endemism and radiated recently in the Pliocene. We aimed to (1) compile an extensive Atlantic Forest data set on vascular, non-vascular plants (including hemiepiphytes), and lichen epiphyte species occurrence and abundance; (2) describe the epiphyte distribution in the Atlantic Forest, in order to indicate future sampling efforts. Our work presents the first epiphyte data set with information on abundance and occurrence of epiphyte phorophyte species. All data compiled here come from three main sources provided by the authors: published sources (comprising peer-reviewed articles, books, and theses), unpublished data, and herbarium data. We compiled a data set composed of 2,095 species, from 89,270 holo/hemiepiphyte records, in the Atlantic Forest of Brazil, Argentina, Paraguay, and Uruguay, recorded from 1824 to early 2018. Most of the records were from qualitative data (occurrence only, 88%), well distributed throughout the Atlantic Forest. For quantitative records, the most common sampling method was individual trees (71%), followed by plot sampling (19%), and transect sampling (10%). Angiosperms (81%) were the most frequently registered group, and Bromeliaceae and Orchidaceae were the families with the greatest number of records (27,272 and 21,945, respectively). Ferns and Lycophytes presented fewer records than Angiosperms, and Polypodiaceae were the most recorded family, and more concentrated in the Southern and Southeastern regions. Data on non-vascular plants and lichens were scarce, with a few disjunct records concentrated in the Northeastern region of the Atlantic Forest. For all non-vascular plant records, Lejeuneaceae, a family of liverworts, was the most recorded family. We hope that our effort to organize scattered epiphyte data help advance the knowledge of epiphyte ecology, as well as our understanding of macroecological and biogeographical patterns in the Atlantic Forest. No copyright restrictions are associated with the data set. Please cite this Ecology Data Paper if the data are used in publication and teaching events. © 2019 The Authors. Ecology © 2019 The Ecological Society of Americ

Enhancing the Reliability of Perception Systems using N-version Programming and Rejuvenation

Machine Learning (ML) has become indispensable for real-world complex systems, such as perception systems of autonomous systems and vehicles. However, ML-based systems are sensitive to input data, faults, and malicious threats that can degrade output quality and compromise the complete system's correctness. Ensuring a reliable output of ML-based components is crucial, especially for safety-critical systems. In this paper, we investigate architectures of perception systems using N-version programming for ML to mitigate the dependence on a singular ML component and combine it with a time-based rejuvenation mechanism to maintain a healthy system over extended periods. We propose models and functions to evaluate the reliability of N-version perception systems subject to faults, malicious threats, and rejuvenation. Our numerical experiments show that a rejuvenation mechanism could benefit a multiple-version system, with a reliability improvement superior to 13%. Also, the results indicate that rejuvenation could improve output reliability when ML modules' accuracy is high

Confirmed-Location Group Membership for Intrusion-Resilient Cooperative Maneuvers

peer reviewedCooperation among autonomous vehicles is required whenever efficiency or safety prevents maneuvering based solely on the information of individuals. Intersection crossing is a prominent example of such a situation, where obstructed views create safety concerns and where driving on sight would lead to known inefficient solutions. However, communication, a prerequisite for cooperation, and, in general, the complexity of autonomous driving stacks elevate the threat surface beyond justifiable thresholds, creating the potential for cyberattacks to succeed, particularly when targeting the “brain”. Some of these attacks go undetected and may harm passengers, pedestrians, and other traffic participants in a vehicle’s proximity. In this paper, we address a fundamental challenge of intrusion-resilient maneuver planning: the question of forming consensus groups given variations in the number N of vehicles that participate in complex maneuvers and given that in a larger group of cars, a larger number F may have already been compromised by an adversary. Introducing confirmed-location-based group membership, we show how trust-anchor-provided precise location information can be leveraged to establish a ground truth about N and F to efficiently solve and agree upon intersection crossing as representative of other complex maneuvers in an F fault-and-intrusion-tolerant manner

Security Modeling and Analysis of Moving Target Defense in Software Defined Networks

The use of traditional defense mechanisms or intrusion detection systems presents a disadvantage for defenders against attackers since these mechanisms are essentially reactive. Moving target defense (MTD) has emerged as a proactive defense mechanism to reduce this disadvantage by randomly and continuously changing the attack surface of a system to confuse attackers. Although significant progress has been made recently in analyzing the security effectiveness of MTD mechanisms, critical gaps still exist, especially in maximizing security levels and estimating network reconfiguration speed for given attack power. In this paper, we propose a set of Petri Net models and use them to perform a comprehensive evaluation regarding key security metrics of Software-Defined Network (SDNs) based systems adopting a time-based MTD mechanism. We evaluate two use-case scenarios considering two different types of attacks to demonstrate the feasibility and applicability of our models. Our analyses showed that a time-based MTD mechanism could reduce the attackers’ speed by at least 78% compared to a system without MTD. Also, in the best-case scenario, it can reduce the attack success probability by about ten times

Stochastic Models for Planning VLE Moodle Environments based on Containers and Virtual Machines

Moodle Virtual Learning Environments (VLEs) represent tools of a pedagogical dimension where the teacher uses various resources to stimulate student learning. Content presented in hypertext, audio or vídeo formats can be adopted as a means to facilitate the learning. These platforms tend to produce high processing rates on servers, large volumes of data on the network and, consequently, degrade performance, increase energy consumption and costs. However, to provide eficiente sharing of computing resources and at the same time minimize financial costs, these VLE platforms typically run on virtualized infrastructures such as Virtual Machines (VM) or containers, which have advantages and disadvantages. Stochastic models, such as stochastic Petri nets (SPNs), can be used in the modeling and evaluation of such environments. Therefore, this work aims to use analytical modeling through SPNs to assess the performance, energy consumption and cost of environments based on containers and VMs. Metrics such as throughput, response time, energy consumption and cost are collected and analyzed. The results revealed that, for example, a cluster with 10 replicas, occupied at their maximum capacity, can generate a 46.54% reduction in energy consumption if containers are used. Additionally, we validate the accuracy of the analytical models by comparing their results with the results obtained in a real infrastructure