bluuebunny/crossref_metadata_2025 huggingface dataset:
Automatic identification of web API change types in consumer applications
Authors: Vidal Graciela , Herrera Franco , Casas Sandra
Published: August 2022

La evolución de las API web afecta a las aplicaciones consumidoras. Su modificación y/o adaptación para que sea compatible con los cambios de las APIs suele ser crítica, ya que es manual, y requiere mayores costes y esfuerzos. Estudios previos registran tipos y/o patrones de cambios en APIs web. Sin embargo, las propuestas que ayuden a los desarrolladores a identificar automáticamente cambios en sus aplicaciones son insuficientes. Este trabajo propone un enfoque que permite identificar automáticamente un conjunto de tipos de cambios. La estrategia consiste en utilizar un diccionario de APIs consumidas y la verificación automática de inconsistencias con la especificación actual de las APIs incluidas en el diccionario.
On the Need to Use Smart Contracts in Enterprise Application Integration
Authors: Parahyba Fernando , Dornelles Eldair F. , Roos-Frantz Fabrícia , Frantz Rafael Z. , Molina-Jiménez Carlos , Reina Quintero Antonia M. , Bocanegra José , Sawicki Sandro
Published: August 2022

Integration processes involve Business Constraints and Service Level Agreements that, with current technology, are not monitored or enforced automatically at run-time. This approach leaves the participants with no means of supervising the development of their interactions or of collecting indisputable evidence to ease the resolution of disputes that can potentially emerge. In this paper, to address the issue, we suggest the inclusion of smart contracts in integration processes to supervise and mediate, at run-time, the agreements to which the participants commit. We discuss the requirements that smart contracts for integration processes need to meet and the challenges involved in writing, executing, deploying, and verifying them.
Outsourcing with distributed teams in large-scale environments
Authors: Camara Rafael , Marinho Marcelo , Sampaio Suzana , Honório Ivaldir , Moura Hermano
Published: August 2022

Outsourcing projects continue to grow through a more distributed and connected environment among teams from all over the globe. Due to it, many customer-vendor relationships are being formed in multi-site settings around a large-scale agile project. Companies are constantly looking for suppliers to assist and develop their complex products, although large-scale agile projects with many teams require some attention to the outsourcing project. Based on it, this study aims to conduct a systematic literature review regarding how outsourced distributed teams from large-scale agile projects are handling their operation. From 23 studies, it was possible to synthesize six distinct themes and 15 patterns of outsourcing distributed teams working in large-scale contexts.
Perceptions of knowledge management in Brazilian software development companies
Authors: dos Santos Juliana Oliveira , Martins Guilherme Augusto dos Reis , de Souza Érica Ferreira , Felizardo Katia Romero , Meinerz Giovani Volnei
Published: August 2022

The software development companies conduct activities that generate a considerable amount of knowledge. Knowledge Management (KM) allows working with the generated knowledge helping in organizational learning. However, professionals in software companies still face several challenges to articulate and leverage knowledge in the organization. We aim at providing evidence about how KM has been adopted in practical environments of software development. We designed a survey instrument that was distributed to Brazilian software development professionals. The survey results improved the current understanding of KM and how it manifests itself in practical software development environments.
Tratamento de Requisitos de Confiabilidade de Sistemas-de-Sistemas: Um Mapeamento Sistemático da Literatura
Authors: Molina Sidny de Almeida , Paiva Débora Maria Barroso , Cagnin Maria Istela
Published: August 2022

Sistemas-de-Sistemas (SoS) é um agrupamento de sistemas de software de diversas organizações que formam alianças de organizações. Esses sistemas trabalham em conjunto para alcançar objetivos de negócio mais complexos, não obtidos por um único sistema de software. Devido às características do SoS, seus requisitos mudam ao longo do tempo, trazendo desafios para a Engenharia de Requisitos. O objetivo deste artigo é descobrir como os requisitos de confiabilidade são tratados durante o ciclo de vida do SoS. Evidenciou-se que esse tipo de requisito está sendo geralmente considerado em fase posterior à fase de requisitos e que há carência de técnicas para tratá-los de maneira apropriada.
Blockchain Governance using Reference Architectures
Authors: Florez Luis
Published: August 2022

Although Blockchain has grown in importance, great barriers still remain, including high failure rates, governance concerns and issues with quality attributes. These barriers have complex interactions and many trade-offs which suggests the need for design tools such as reference governance models and reference architectures. However, these tools are scarce and research in this area, especially governance, is sparse. The object of this research is thus to propose and then validate a new reference governance model and reference architecture to better design systems with desired attributes. The research will be conducted using the design science methodology and validated through case studies.
Evaluation and Improvement of Narrative Visualizations
Authors: Lezcano Airaldi Andrea
Published: August 2022

In the field of Information Visualization, storytelling techniques help to communicate facts and enhance comprehension. The use of data storytelling best practices can inform the process of creating narrative visualizations and increase the quality of charts used in software applications by improving aspects such as memorability or engagement, for instance, supporting end users in the decision-making process. The main goal of this doctoral research is to develop a method for assessing and improving the quality of narrative visualizations in software products, like scatter plots, line, bar, or pie charts, among others. This has included a case study and a systematic mapping study.
How to Identify Programming Skills from Source Code?
Authors: Oliveira Johnatan
Published: August 2022

Both open-source and proprietary software systems have become increasingly complex. Despite their growing complexity and increasing size, software systems must satisfy strict release requirements that impose quality, putting significant pressure on developers. Therefore, the success of software projects is dependent on the identification and hiring of qualified developers to build a solid and cohesive team with different programming skills. Our main goal is to develop and evaluate a method able to compute programming skills from source code analysis. Our method uses software metrics such as Changed Files and Changed Lines of Code, to compute the skills. Our results showed that our method is able of identifying programming skills of the developers about mainly libraries used, programming languages, and profile concerning back-end & front-end and unit test.
On the Continuous Delivery in IoT Systems
Authors: Pereira Igor Muzetti
Published: August 2022

The development of IoT systems brings the adequacy of software engineering to current paradigms, such as IoT, industry 4.0, smart cities and environments, and wearable devices. IoT systems require the integration of different technologies such as sensors, actuators, edge devices, cloud computing, big data, artificial intelligence, and IT operations. The successful delivery of products depends directly on the continuous cooperation of professionals with different skills. However, we noticed a specific resistance in carrying out continuous and automated software deliveries to hardware devices by professionals. This research investigates the practices and technologies for the continuous software delivery pipeline on hardware devices that make up an IoT system.
Analysis of Network Performance over Deep Reinforcement Learning Control Loops for Industry 4.0
Authors: Bernardo Guilherme T. T. , Miranda Jr. Gilson , Macedo Daniel F.
Published: August 2022

One of the three core use cases of 5G is ultra-reliable low-latency communications, which encompasses remote operation for Industry 4.0. It is expected that autonomous robots, driven by artificial intelligence agents running on the cloud, will operate without human assistance. This paper investigates how the network performance affects the behavior of AI-based remote operation control that is based on deep reinforcement learning. We investigate two separate aspects: (i) the performance of a pre-trained model on ideal network conditions and (ii) how the varying network conditions impact the agent performance in the environment. The results show the influence of different network quality conditions on agent's performance, as well as the benefit of using agents pre-trained on ideal conditions.
Escalonamento de Tarefas Ciente de Contexto para Computação de Borda Veicular
Authors: Costa Joahannes B. D. da , Souza Allan M. de , Meneguette Rodolfo I. , Cerqueira Eduardo , Rosário Denis , Villas Leandro A.
Published: August 2022

A Computação de Borda Veicular é um paradigma promissor que fornece serviços de computação em nuvem próximo dos usuários veiculares. Veículos e infraestruturas de comunicação podem cooperativamente atender serviços veiculares com restrições de baixa latência através da formação de nuvens veiculares e utilização dos recursos computacionais dessas nuvens, sendo este último processo chamado de escalonamento de tarefas. Um escalonador de tarefas eficiente precisa decidir qual nuvem veicular executará as tarefas, levando em consideração aspectos como mobilidade veicular e requisitos das tarefas. Isso se faz importante para minimizar o tempo de processamento e o custo monetário na utilização do poder computacional. No entanto, as soluções da literatura não consideram esses aspectos contextuais conjuntamente, o que pode degradar a eficiência geral do sistema. Assim, apresenta-se o CARONTE, um escalonador de tarefas que considera aspectos contextuais em seu processo de decisão. Os resultados mostraram que o CARONTE é capaz de escalonar mais tarefas enquanto minimiza custo monetário, atraso de computação e tempo de fila, quando comparado com abordagens do estado da arte.
Um Método de Ofuscação para Proteger a Privacidade no Tráfego da Rede IoT
Authors: Santos Bruna V. dos , Vergutz Andressa , Nogueira Michele , Macedo Ricardo T.
Published: August 2022

A Internet das Coisas (IoT) conecta objetos à Internet, oferecendo serviços e aplicações inteligentes. Por meio da análise do tráfego da rede, adversários identificam dispositivos, padrões e até mesmo os comportamentos dos usuários, comprometendo seriamente a privacidade. Na literatura, trabalhos propuseram o uso de técnicas de ofuscação do tráfego da rede para evitar ataques. Entretanto, ainda existem brechas exploradas pelos adversários visto que nem todo tráfego da rede é mascarado, sem mencionar o tradeoff entre privacidade e sobrecarga da rede. Este trabalho apresenta um método de ofuscação do tráfego da rede para melhorar a privacidade dos usuários enquanto mantém uma baixa sobrecarga da rede IoT. Particularmente, o método toma como base a técnica de geração de tráfego falso seguindo diferentes níveis de ofuscação. Tais níveis permitem flexibilizar a sobrecarga na rede enquanto melhora a privacidade. Os resultados mostram a ofuscação do tráfego dos dispositivos IoT, reduzindo em até 42% a precisão de identificação dos dispositivos da IoT.
UserPoint-MAG: Abordagem de rede multicamada para o estudo de propagação de contágio no transporte público
Authors: Santin Priscila , Gubert Fernanda R. , Fonseca Mauro , Munaretto Anelise , Silva Thiago H.
Published: August 2022

Em tempos de pandemia, o transporte público pode ser crucial para a disseminação de vírus, principalmente nas grandes cidades. As vacinas costumam fazer parte das estratégias para reduzir o contágio; no entanto, estas podem ser escassas em cenários pandêmicos. Utilizando dados do sistema de transporte público, este trabalho propõe o uso de redes multicamadas variantes no tempo para identificar os principais locais críticos a serem considerados prioritários em intervenções, como campanhas de vacinação, para ajudar a reduzir o contágio nesse meio de locomoção. Nossa abordagem considera os pontos de ônibus críticos como pontos prioritários de vacinação, indicando que a vacinação nesses locais reduz a propagação da infecção usando menos doses do que uma vacinação aleatória. A abordagem proposta neste estudo não se limita às estratégias de vacinação, sendo também aplicável a outros problemas que compartilham propriedades semelhantes, mesmo em contextos diferentes.
Machine Learning-Based Intrusion Detection System for Automotive Ethernet: Detecting Cyber-Attacks with a Low-Cost Platform
Authors: Carmo Pedro R. X. , Araujo-Filho Paulo Freitas de , Campelo Divanilson R. , Freitas Eduardo , Oliveira Filho Assis T. de , Sadok Djamel F. H.
Published: August 2022

Automotive Ethernet is being adopted in vehicles to provide the larger throughput that is required by autonomous vehicles. However, these vehicles may be subject to several cyber-attacks that compromise their operation and passengers' safety. This work proposes an Intrusion Detection System (IDS) that detects stream injection attacks on automotive Ethernet networks. The proposed IDS is based on feature generation and the XGBoost machine learning algorithm. Experimental results show that our proposed IDS achieves 0.9805 of AUCROC and a detection time of 620µs that allows real-time intrusion detection while using an inexpensive hardware platform, such as a Raspberry Pi. This is extremely important as cost is one of the automotive industry's main concerns.
Detecção de ataques DDoS usando correlação espaço-temporal bayesiana
Authors: Mendonça Gabriel , Santos Gustavo H. A. , Souza e Silva Edmundo de , Leão Rosa M. M.
Published: August 2022

Ataques DDoS têm causado prejuízos consideráveis ao longo dos anos. Para mitigar seu impacto, a detecção deve ocorrer preferencialmente próximo à origem. Propomos neste trabalho um sistema leve de detecção de DDoS que usa apenas contadores de bytes e pacotes de roteadores domésticos. Para detectar ataques com informações limitadas, empregamos duas camadas: (1) um classificador treinado com dados reais de usuários domésticos; (2) um modelo hierárquico bayesiano que correlaciona alarmes de várias residências. Usamos código-fonte de malwares reais para gerar tráfego de ataque DDoS nas casas de um grupo de voluntários durante 31 dias. Os experimentos realizados em campo mostraram que nosso sistema possui excelente desempenho.
TCPBP: Enhancing the TCP Throughput in High Speed Lossy Networks With a Single Mixed Routing Metric
Authors: Vilela Hugo Christ , Fonseca Keiko Verônica Ono , Fonseca Mauro Sérgio Pereira
Published: August 2022

In this work, we present Transmission Control Protocol Best Path (TCPBP), an alternative link-state routing metric aims to optimize single-path TCP throughput on intradomain High Speed Lossy Networks. The enhanced performance is achieved by combining delay and packet loss on a single mixed metric as a function parameter. In contrast, this function predicts the TCP throughput that should be maximized. As proof of concept, TCP bandwidth tests were executed in an SDN High Speed Lossy Network Linux-emulated topology with ten nodes, employing an intradomain link-state routing protocol (i.e., OSPF-like) capable of running both TCPBP and traditional metrics delay and hop-count. Such topology was chosen because of the potential to improve its TCP throughput with route selection, given the characteristics of the topology itself and the TCP throughput prediction model adopted. The overall TCP throughput performance obtained with TCPBP was greater than with different routes founded by traditional metrics, showing the potential of our proposal.
NEMESIS: Mecanismo para Formação de Nuvens Veiculares Baseado em Previsão de Mobilidade
Authors: Costa Joahannes B. D. da , Lobato Junior Wellington V. , Souza Allan M. de , Cerqueira Eduardo , Rosário Denis , Villas Leandro A.
Published: August 2022

Levando em consideração a rápida modernização dos veículos, que os direciona para entidades cada vez mais inteligentes e conectadas, surge o paradigma de Computação de Borda Veicular. Esse paradigma fornece serviços de computação em nuvem mais próximos dos usuários veiculares através da agregação dos recursos de veículos, a qual é denominada formação de nuvens veiculares. No entanto, devido à alta dinamicidade do ambiente veicular, agregar e utilizar esses recursos impõe alguns desafios. O principal desafio é selecionar de forma eficiente quais veículos irão assumir papéis de gerenciamento na utilização do poder computacional, os chamados veículos líderes. Sendo assim, este trabalho apresenta o NEMESIS, um mecanismo baseado em previsão de mobilidade para formação de nuvens veiculares. O NEMESIS seleciona os veículos com maior tempo de permanência nas nuvens para gerenciar o processo de utilização dos recursos. Os resultados de simulação mostraram que o NEMESIS consegue aumentar o tempo de vida das nuvens, minimizar as mudanças de líderes, enviar menos mensagens na rede, o que colabora na diminuição da colisão de pacotes, e por fim, possibilita uma utilização eficiente dos recursos veiculares agregados.
Vertical Parallelization of Differential Evolution Heuristic for Network Slicing in 5G Scenarios
Authors: Gomes Rayner , Vieira Dario , Castro Miguel Franklin de
Published: August 2022

The 5G mobile network is based on a virtualized infrastructure and offers a virtual network (VN) creation service considering many new scenarios arising from the 5G vision. The diversity of scenarios and the instantiation of VN on-demand induce pressure on the virtual network embedding (VNE). VNE is the mapping of virtual nodes and links to real nodes and links obeying the QoS parameters present in the VN request and available resources. Since this is an optimization and NP-Hard problem, multiple efforts have been made to create VNE algorithms. Considering such efforts, this work presents: (i) a fitness function regarding multiobjective optimization; and (ii) a Parallel Differential Evolution (PDE) approach to face the VNE. We designed the PDE due to the lack of viable parallel solutions in the 5G scenario. We compared our approaches with different versions of Greedy, Stress, and Genetic Algorithms, totaling ten approaches. The results demonstrate that DE and its parallel version obtained a higher number of mapped requisitions. Also, the parallel performance decreases the execution time in certain conditions; in a favorable scenario, the parallel version obtains up 21.04% of runtime reduction.
Um Mecanismo de Proteção Ciente de Vias Aéreas Contra Jamming Attacks para a Internet dos Drones
Authors: Svaigen Alisson R. , Boukerche Azzedine , Ruiz Linnyer B. , Loureiro Antonio A. F.
Published: August 2022

A Internet dos Drones (IoD) é um paradigma de rede no qual os drones voam por vias aéreas bem definidas. O Jamming Attack (JA) representa um risco grave para a IoD, podendo afetar a trajetória dos drones. Embora o JA tenha sido investigado em redes de UAVs, as soluções existentes consideram o espaço aéreo livre para voo, enquanto na IoD os drones voam num espaço aéreo restrito. Para superar esse desafio, este trabalho apresenta o projeto de um mecanismo de proteção de vias aéreas contra JA em IoD. Nossos experimentos mostraram que nossa solução propõe um mecanismo de proteção adequado para evitar que drones sofram um JA, diminuindo o número de drones afetados. Assim, este trabalho avança os mecanismos de proteção para IoD.
Caracterização Escalável de Vulnerabilidades de Segurança: um Estudo de Caso na Internet Brasileira
Authors: Ponce Lucas M. , Gimpel Matheus , Fazzion Elverton , Cunha Ítalo , Hoepers Cristine , Steding-Jessen Klaus , Chaves Marcelo H. P. C. , Guedes Dorgival , Meira Jr. Wagner
Published: August 2022

Serviços de monitoramento como o Shodan são cada vez mais populares no rastreamento de aplicações e vulnerabilidades na Internet. Neste artigo caracterizamos e discutimos vulnerabilidades encontradas na Internet brasileira utilizando dados de monitoramento provenientes do Shodan. Além disso, discutimos métodos de Ciências dos Dados para melhorar a escalabilidade e a qualidade de análises; também combinamos metadados de fontes complementares sobre rede e vulnerabilidades para extrair resultados e conclusões mais assertivos. Nossa caracterização expõe diversas vulnerabilidades de alta severidade na rede brasileira, algumas catalogadas há mais de cinco anos e que continuam prevalentes até hoje. Esperamos que as análises apresentadas neste artigo incentivem organizações a implantarem atualizações e mecanismos de proteção para mitigar essas ameaças.
Preempção Condicional de Pacotes baseada na Vida Média Residual para Otimização da Age of Information em Sistemas Ciberfísicos
Authors: Prandel Paulo César , Barreto Priscila Solis
Published: August 2022

No estudo de Sistemas Ciberfísicos, a Age of Information (AoI) surge como um novo conceito ao representar o grau de atualização da informação que um monitor possui em relação a uma entidade ou processo remoto que envia atualizações periodicamente. O gerenciamento de pacotes é um conjunto de técnicas e políticas que priorizam, bloqueiam ou executam a preempção (descarte e substituição) de pacotes que chegam a um servidor e que pode ser utilizada para otimização da AoI. As técnicas no estado da arte, como a LGFS (do inglês Last Generated First Served) possuem comportamento estático, executando a mesma ação para todos os pacotes: LGFS-S executa a preempção em serviço e LGFS-W e executa na fila de espera. O presente estudo propõe uma nova técnica de preempção condicional chamada LGFS-C, a qual utiliza o conceito de vida média residual para decidir a política a ser aplicada a um pacote em serviço ou na fila de espera. Para um modelo de fila simples, demonstra-se que a técnica LGFS-C alcança sempre resultados iguais ou superiores aos das outras técnicas, para qualquer distribuição de tempo de serviço para o servidor. A técnica proposta foi validada através de simulações, com o uso de uma ferramenta computacional especialmente desenvolvida para esse propósito.
Recuperação do Serviço de Pelotão de Veículos Autônomos e Conectados Baseado em Modelos Virtuais Contra Ataques na Rede e Sensores
Authors: Andrade Everaldo , Santos Aldri
Published: August 2022

A formação em pelotão de veículos autônomos e conectados (VACs) é a estrutura topológica mais comum de VACs, onde veículos trafegam relativamente próximos, respondendo aos comandos do líder e provendo serviços de transporte autônomo e inteligente. Contudo, com a evolução dos pelotões de VACs vários tipos de ataques de injeção de dados falsos em redes e sensores têm comprometido a estabilidade do pelotão, resultando na fragmentação ou, no pior caso, em serias colisões. Este trabalho propõe um mecanismo, denominada PReCAV, para a recuperação cooperativa de pelotão de VACs diante de ataques de injeção de dados falsos, apoiada por modelos virtuais e físicos do sistema, a fim de manter resiliência em meio à ataques em rede e sensores. Uma avaliação por simulação mostrou que o PReCAV manteve a estabilidade do pelotão ao prover resiliência, privando-o de variações de 4,42m/s2 de aceleração, 13m/s de velocidade e 40m de distância entre veículos em ataques na rede e variações de 4,43m/s2, 10,7m/s e 33,4m em ataques nos sensores.
CACIC: Controle de Acesso Confiável Usando Enclaves a Dados em Nuvem da Internet das Coisas
Authors: Thomaz Guilherme A. , Guerra Matheus B. , Sammarco Matteo , Campista Miguel Elias M.
Published: August 2022

Os sensores da Internet das Coisas geram um enorme volume de informações sensíveis processadas em nuvem, como geolocalização, consumo de energia e sinais biomédicos. Os sistemas de controle de acesso convencionais falham em garantir que esses dados sejam acessados e modificados apenas por usuários autorizados em um cenário em que a nuvem é maliciosa. Este artigo propõe uma arquitetura para transmissão, armazenamento, processamento e disponibilização de dados sensíveis de Internet das Coisas em nuvens computacionais, utilizando ambientes de execução confiáveis. Diferente das arquiteturas convencionais, o cliente personaliza para quem os seus dados são disponibilizados, pois as publicações e consultas aos dados são processadas em regiões isoladas na memória chamadas de enclaves. A implementação do servidor seguro processa cerca de quinhentas requisições por segundo e o atraso inserido pelo processamento em enclaves de memória é igual a dezessete milissegundos. Os resultados revelam que a proposta é ágil e escalável, ao mesmo tempo que garante segurança, até mesmo quando o sistema operacional ou o administrador da nuvem são comprometidos.
Abordagem Adaptativa para Proteção de Redes SDN Utilizando Moving Target Defense
Authors: Nunes Rodrigo S. S. , Pascoal Túlio , Souza Cristian H. M. , Neto Emídio , Silva Felipe S. Dantas
Published: August 2022

O paradigma das Redes Definidas por Software (SDN) vem sendo cada vez mais adotado nas infraestruturas de redes de computadores. No entanto, por se tratar de uma abordagem relativamente recente, poucas estratégias de segurança tem sido empregadas para sua proteção. Um dos problemas relacionados ao uso de SDN é a presença de ataques do tipo scanning. Recentemente, as técnicas de Moving Target Defense (MTD) tem sido usadas para lidar com esses ataques. No entanto, devido à sua natureza de operação, as estratégias baseadas em MTD podem degradar consideravelmente o desempenho da rede. Assim, com o objetivo de conciliar, de forma factível, a proteção contra ataques scanning sem degradar os serviços da rede, principalmente em termos de Qualidade de Serviço (QoS), neste artigo apresentamos o MTD Adaptive Delay System (MADS). Em nossa proposta, os atrasos baseados em MTD são aplicados apenas à resposta dos pacotes quando a rede está sob ataque. Em contraste, os trabalhos existentes aplicam continuamente atrasos à todos os pacotes trafegados e, portanto, incorrem em mais degradação na rede. MADS foi extensivamente avaliado e comparado com o estado da arte em defesas baseadas em MTD. Os resultados das avaliações demonstraram que MADS promove menor degradação na rede em termos de latência, pacotes Bad TCP e vazão.
Towards a High Efficiency of Native NDN over Wi-Fi 6 for the Internet of Vehicles
Authors: Sena Ygor Amaral B. L. de , Dias Kelvin Lopes
Published: August 2022

Named Data Networking (NDN) is a top-notched architecture to deal with content distribution over the Internet. With the explosion of video streaming transmission and future massive Internet of Things and Vehicles (IoT/IoV) traffic, evolving Wi-Fi networks will play an essential role in such ecosystems. However, Native NDN deployment over wireless networks may not perform well. Wi-Fi broadcasts/multicasts result in reduced throughput due to the usage of basic service mode. Despite recent initial works addressing that issue, further studies and proposals are required to boost the adoption of Native NDN. We advocate that an initial step towards designing a feasible Native NDN over wireless networks should be understanding the challenges in emerging scenarios and providing a uniform baseline to compare and advance proposals. To this end, first, we highlight some challenges and directions to improve throughput and energy efficiency, reduce processing overhead, and security issues. Next, we propose a variant of NDN that minimizes the problems identified by performing transmission via unicast to avoid storms in wireless networks. Finally, we conducted a performance evaluation to compare Standard Native NDN with our proposal on Wi-Fi 6 vehicular networks. The results show that our proposal outperforms the Standard NDN in the evaluated scenarios, reaching values close to 89% of satisfied requests, achieving more than 200% of data received than Standard NDN.
OpenPCI: Um toolkit para atender os requisitos técnicos do PCI DSS
Authors: Dapper Fábio Juliano , Fagundes Leonardo Lemes
Published: August 2022

Este artigo não possui resumo.
Acordo de Chave sem Certificados sob Emissão de Múltiplas Chaves Públicas
Authors: Goya Denise , Rufino Vilc , Terada Routo
Published: August 2022

Este artigo não possui resumo.
S-Process: Um Processo para Desenvolvimento de Aplicações Seguras
Authors: Azevedo Ryan Ribeiro de , Almeida Silas Cardoso de , Dantas Eric Rommel Galvão , Veras Wendell Campos , Abella Daniel , Rocha Rodrigo C. G.
Published: August 2022

Este artigo não possui resumo.
Um Modelo de Controle Formal para o gerenciamento de riscos de processo em fábricas de software
Authors: Cardoso Felipe Rafael Motta , Carvalho Strauss Cunha , Douradinho Danilo , Montini Denis Ávila , Tasinaffo Paulo Marcelo , Cunha Adilson Marques da
Published: August 2022

Este artigo não possui resumo.
The Paepalanthoideae (Eriocaulaceae) of the Chapada dos Veadeiros National Park, Brazil: taxonomic novelties, identification key, and illustrated list of species
Authors: Trovó Marcelo , Echternacht Lívia
Published: August 2022

The Chapada dos Veadeiros National Park is a conservation unit established to preserve the highest savannahs of Central Brazil and their unique biodiversity. Eriocaulaceae are a relevant and conspicuous family in such high savannahs, but its diversity is poorly known, documented solely in general lists or in isolated efforts aimed at small groups. After a structured series of field expeditions and analysis of specimens from the relevant herbaria, we provide nomenclatural novelties, a first identification key, and an illustrated checklist for the species of Paepalanthoideae (Eriocaulaceae) in the area. We recorded 42 species of Paepalanthoideae from the Chapada dos Veadeiros National Park: Actinocephalus (Körn.) Sano (2 spp.), Comanthera L.B.Sm. (1 sp.), Paepalanthus Mart. (24 spp.), and Syngonanthus Ruhland (15 spp.). Actinocephalus brevifolius Trovó & Echtern. sp. nov. and P. irwinii Trovó & Echtern. sp. nov. are newly described species and P. politus Trovó stat. et nom. nov. is a variety of P. elongatus (Bong.) Körn. raised to the species status with a new name. The generic and specific composition shows predominance of Paepalanthus and Syngonanthus, and with a low representation of Actinocephalus and Comanthera, as expected, outside of the Espinhaço Range. More than 50% of the species (22 spp.) are endemic to the area and 25 species are endemic to Central Brazil, the area being the main center of diversity for dimerous-flowered groups. The non-endemic diversity is a combination of widespread species and marginal distribution of species typical from the Amazon and southeastern savannahs. The species are unevenly distributed in the area, with their occurrence correlated to altitude, water availability, and lithology. We reinforce that the savannahs from Central Brazil are a secondary center of diversity for Eriocaulaceae, playing a central role in the conservation of an unique and irreplaceable piece of its diversity and the Cerrado biome as well.
Higher education resource optimal allocation and relationship with the biopharma and biotech industry
Authors: Li Qing
Published: August 2022

Current educational resource allocation methods achieve resource allocation through a simple linear constraint approach to a single resource restriction, and the single resource allocation restriction causes the allocation results to easily fall into a local optimal solution, which not only has low allocation efficiency but also cannot allocate all the resources effectively, leading to the waste of educational resources of a certain category or several categories in pharma industries. To address the above problems, the algorithm of optimal allocation of higher education resources based on the relationship with the biopharma and biotech industry is proposed. In view of the above problems, an algorithm for optimizing the allocation of higher education resources biopharma and biotech industry is proposed. By establishing a multidimensional analysis model of educational resource data, the resource data are analyzed and processed to determine the factors affecting resource allocation. Combined with the actual establishment of the resource allocation optimization model, NSGA-II is used to solve the problem, and the optimal allocation scheme is obtained. The application of the proposed configuration algorithm to reduce the standard deviation of the resource discrepancy coefficient to 0.134 is demonstrated through simulation experiments, which can effectively reduce the discrepancy between resources.
The programming method of MATLAB language for solving AI based digital health problems
Authors: Jiang Shihui
Published: August 2022

In recent years, the fractional calculus equation has been widely used in many application disciplines, and its numerical solution accuracy and efficiency requirements are also higher and higher. MATLAB is used to solve mathematical problems has been very common, can quickly and intuitively solve the equation. Therefore, we will study the programming method of MATLAB language to solve the digital health problems and linear algebra problems of calculus. According to the general form of fractional order linear constant coefficient multinomial differential equation, the equation model is established. The shifted Jacobi polynomials are deformed by definition and linear algebra theory. Using the function approximation theory, the numerical calculation algorithm of calculus is designed. Use C++ to call the function encapsulation in MATLAB to complete the program design of the AI-based program used for solving health problems and algebra problems. Through the actual calculation example, it is verified that the solution precision of the method used in the study can reach 10-4, which meets the solution precision and can be used to solve practical problems.
Does application of naphthenic acids in early fruit development stage result in prolonged effect on cold storage and shelf life of apricot fruit?
Authors: Kevrešan Žarko , Milić Biserka , Bajić Aleksandra , Kovač Renata , Milović Maja , Kalajdžić Jelena , Barać Gordana
Published: August 2022

In this experiment, the effects of the application of naphthenic acids (NAs) on apricots in the early fruit development phases on fruit morphological properties, composition and postharvest properties were investigated. Two concentrations of NAs (1 mg/L and 3 mg/L) were applied at two development stages in the apricot cultivar NS-4. The application of NAs at the beginning of the petal fall development phase resulted in the reduction of fruit dimensions at harvest while the application 7 days later increased fruit dimensions. Although there were no significant differences in the most investigated characteristics between fruits treated with NAs and untreated control at harvest or in the postharvest period during 20 days of cold storage (1 ± 1 °C, RD 80%) and particularly after 3 days of shelf life at room temperature. However, compared to the untreated control, apricots treated with NAs were characterized by higher total soluble solids content accompanied by higher fructose content, and lower titratable acidity accompanied by higher succinic acid content. Apricots treated with NAs showed trends towards improved sensory properties: sweeter and less sour taste, with more expressed apricot aroma accompanied with decreased gumminess and crispiness and more intensive tissue breakdown, but without expressed notes of inappropriate taste.
NCCN Guidelines® Insights: Bladder Cancer, Version 2.2022
Authors: Flaig Thomas W. , Spiess Philippe E. , Abern Michael , Agarwal Neeraj , Bangs Rick , Boorjian Stephen A. , Buyyounouski Mark K. , Chan Kevin , Chang Sam , Friedlander Terence , Greenberg Richard E. , Guru Khurshid A. , Herr Harry W. , Hoffman-Censits Jean , Kishan Amar , Kundu Shilajit , Lele Subodh M. , Mamtani Ronac , Margulis Vitaly , Mian Omar Y. , Michalski Jeff , Montgomery Jeffrey S. , Nandagopal Lakshminarayanan , Pagliaro Lance C. , Parikh Mamta , Patterson Anthony , Plimack Elizabeth R. , Pohar Kamal S. , Preston Mark A. , Richards Kyle , Sexton Wade J. , Siefker-Radtke Arlene O. , Tollefson Matthew , Tward Jonathan , Wright Jonathan L. , Dwyer Mary A. , Cassara Carly J. , Gurski Lisa A.
Published: August 2022

The NCCN Guidelines for Bladder Cancer provide recommendations for the diagnosis, evaluation, treatment, and follow-up of patients with bladder cancer and other urinary tract cancers (upper tract tumors, urothelial carcinoma of the prostate, primary carcinoma of the urethra). These NCCN Guidelines Insights summarize the panel discussion behind recent important updates to the guidelines regarding the treatment of non–muscle-invasive bladder cancer, including how to treat in the event of a bacillus Calmette-Guérin (BCG) shortage; new roles for immune checkpoint inhibitors in non–muscle invasive, muscle-invasive, and metastatic bladder cancer; and the addition of antibody–drug conjugates for metastatic bladder cancer.
The Python Package open-crypto: A Cryptocurrency Data Collector
Authors: Günther Steffen , Fieberg Christian , Poddig Thorsten
Published: August 2022

This paper introduces the package open-crypto for free-of-charge and systematic cryptocurrency data collecting. The package supports several methods to request (1) static data, (2) real-time data and (3) historical data. It allows to retrieve data from over 100 of the most popular and liquid exchanges world-wide. New exchanges can easily be added with the help of provided templates or updated with build-in functions from the project repository. The package is available on GitHub and the Python package index (PyPi). The data is stored in a relational SQL database and therefore accessible from many different programming languages. We provide a hands-on and illustrations for each data type, explanations on the received data and also demonstrate the usability from R and Matlab. Academic research heavily relies on costly or confidential data, however, open data projects are becoming increasingly important. This project is mainly motivated to contribute to openly accessible software and free data in the cryptocurrency markets to improve transparency and reproducibility in research and any other disciplines.
Subpopulation Treatment Effect Pattern Plot (STEPP) Methods with R and Stata
Authors: Venturini Sergio , Bonetti Marco , Lazar Ann A. , Cole Bernard F. , Wang Xin Victoria , Gelber Richard D. , Yip Wai-Ki
Published: August 2022

We introduce the stepp packages for R and Stata that implement the subpopulation treatment effect pattern plot (STEPP) method. STEPP is a nonparametric graphical tool aimed at examining possible heterogeneous treatment effects in subpopulations defined on a continuous covariate or composite score. More pecifically, STEPP considers overlapping subpopulations defined with respect to a continuous covariate (or risk index) and it estimates a treatment effect for each subpopulation. It also produces confidence regions and tests for treatment effect heterogeneity among the subpopulations. The original method has been extended in different directions such as different survival contexts, outcome types, or more efficient procedures for identifying the overlapping subpopulations. In this paper, we also introduce a novel method to determine the number of subjects within the subpopulations by minimizing the variability of the sizes of the subpopulations generated by a specific parameter combination. We illustrate the packages using both synthetic data and publicly available data sets. The most intensive computations in R are implemented in Fortran, while the Stata version exploits the powerful Mata language.
On the Role of Modelling and Simulation for Artificial Intelligence
Authors: Abbass Hussein
Published: August 2022

Artificial Intelligence (AI) is the ubiquitous revolutionary technology of this century. AI has revolutionised humanity, including industry and government organisations, and transformed our world into smart digital spaces. As a discipline, one of the definitions of AI is the automation of cognition; or, put simply, the set of technologies required to support the design and implementation of artificial cognition in artificial systems. To design an intelligent system/machine, technologists need to transform the algorithms of AI into a system-of-systems design of cognition, whereby the artificial agent can sense, make sense, make decisions, take decisions, and learn about the contexts it is situated within. Modelling and Simulation (M&S) sit at the core of an artificial agent’s design and implementation components. In this presentation, I will cover the use of M&S within AI from different angles . In doing so, I will bring elements from my research to showcase how M&S not only contributes to AI but also shows that without M&S, AI can’t operate. I will paint futures that range from very concrete and narrowly defined uses of AI to a world of human and artificial cognitive agents. Humans educate AI agents, and AI agents educate humans. I will then conclude with some challenges for the M&S community to support the effort in advancing AI. This presentation will be drawn from many of my published works; below are some critical references for interested readers.
Modelling Using Deep Learning
Authors: Gupta Manish
Published: August 2022

Machine learning, and in particular, deep learning has emerged as an important tool for advancing science, in addition to its broad based impact on the world. This talk describes three research efforts that illustrate how deep learning can complement modeling and simulation to pursue scientific discoveries and to tackle societal problems. We begin by describing a flood forecasting initiative that has already led to hundreds of thousands of alerts being sent to people in India. It utilizes a new hydrologic model that has been built using an LSTM (long short-term memory) architecture and a physics based inundation model whose effectiveness has been enhanced using machine learning methods. We also describe how self supervised learning is being applied to study several interesting aspects of the organization of the human brain. The generated embeddings can be used to rapidly annotate new structures and develop new ways of clustering and categorizing brain structures based on purely data-driven criteria. Finally, we present a deep learning based modeling of human behavior in a specific game-based setting, which has very interesting implications if we are able to generalize that approach to broader settings.
Simulating the automation of sorting crates – a stepwise approach
Authors: Misund Armand , Gaspar Henrique M.
Published: August 2022

This paper presents an initial study on the task of automating the sorting of crates. It was paramount in the study that the technology used for the automation process is already existing and available to the client in Norway; therefore an existing pallet sorting system was used as state of the art. The simulation was performed via a stepwise approach, that is, first we simulated the process as it is now. Based on this manual case we simulated the same system under a new automated task at time, until the final case (close to) fully automated. A set of KPIs was defined and used in the assessment of all cases, including the manual one. These are Opex [NOK], Capex [NOK], Reliability [Stops over time], Robustness [%, Probability of consequence], and Efficiency [crates per hour]. Our finding overall is that the time it took to sort a given number of crates decreased with increasing implementation of automation through the various change cases. With reduced time consumption the efficiency and number of crates per hour increased, from 300 in the manual case, to 1200 in the fully automated case. The increase in automation also resulted in increased costs, both capital- and operating costs. If we look at the cost increase in relation to the capacity increase, the cost per sorted crate decreases by about 50%. The manual process sorts 300 crates per hour, and if we look at the cost per sorted crate, the fully automated case must sort 600 crates per hour to breakeven and justify the automation.
Rising Regional Importance of the Renminbi in the Asia-Pacific Area: A Panel Analysis
Authors: Boros Eszter , Sztano Gabor
Published: August 2022

In the past decade, China has taken several steps to make the renminbi (RMB) a globally important currency. Since the RMB was included in the IMF’s SDR basket in 2016, several central banks have started using it as a reserve currency. Moreover, the exchange rate regime of the RMB has gradually been modified to add some flexibility, and Beijing has begun to promote cross-border RMB trade settlement. In this paper, we use panel data to analyze how changes in the RMB exchange rate might influence the movement of other currencies in the Asia-Pacific region. We find that the RMB’s relative importance has significantly increased from 2015 onwards, i.e. since the date of the last reform of the RMB central parity quoting mechanism. Our results also suggest that the RMB’s impact has remained broadly unchanged since the outbreak of the coronavirus pandemic. Contrary to some other findings, the COVID‑19 has not reduced the relevance of the Chinese currency in the region.
Identifying regional models of active grandparenting in Russia based on cluster analysis
Authors: Shubat Oksana , Shmarova Irina
Published: August 2022

One of the important social roles the elderly perform is that of grandparents. Our study aims to identify groups of Russian regions with similar models of grandparental activity. The research focuses only on grandmothers. To determine these models, we applied the hierarchical cluster analysis. We used indicators that characterize potential (based on the age criterion) and active (based on intensive involvement in caring for grandchildren) grandparenting in Russian regions. In the process of clustering, we use the growth rates of active grandmothers in the total number of potential grandmothers in 2011-2014, 2014-2016, 2016-2018. The analysis based on the Ward method and the Euclidean distance allowed us to identify 4 models of grandparental activity (regarding grandmothers) in Russian regions. The models differ significantly in the specifics of changes in the degree of grandmothers' involvement in caring for their grandchildren. These models provide the framework for developing specific demographic policy measures with the regional heterogeneity in mind.
Stratification of Timed Petri Nets at the Example of a Production Process
Authors: Simon Carlo , Haag Stefan , Zakfeld Lara
Published: August 2022

Timed dynamic systems can be modeled and simulated with Petri nets using very different approaches. In Clock Pulse Models (CPM), one marking represents exactly one moment in time and all enabled transitions fire simultaneously according to a global clock. This allows for real-time observation of the modeled system during a simulation run. Since simulation time is proportional to observed real-time, such an approach barely scales concerning the observed time horizon. If not the observable behavior over time is of interest but the final simulation result, Event Triggered Models (ETM) can overcome this limitation as has been shown in former publications. A time-lapse model accelerates simulation runs by focusing on the moments state chances occur.CPM and ETM, however, share one disadvantage: the models' sizes tend to be proportional to the number of events that may occur simultaneously in the real world. Hence, they scale barely with the number of modeled entities. This unsolved scaling problem is addressed in this paper and pursues the idea of folding similar subnets in temporal layers called strata. The result is a Stratified Simulation Model (SSM), a small, flexible model that scales well concerning the number of modeled entities. SSM are derivates of CPM, since they still allow for nearly-real-time observation of the real world. Moreover, these models ease the visualization of the simulation in dashboards. The approach is explained at the example of a small production process.