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2024 Brazilian Symposium on Robotics (SBR), and 2024 Workshop on Robotics in Education (WRE)

作者： Matheus Biscaya Gutierrez João Alberto Fabro André Cristiano Cordeiro Academic Department of Informatics (DAINF-CT) Federal University of Technology - Paraná (UTFPR) Curitiba PR Brazil Graduate Program on Applied Computing (PPGCA) DAINF-CT - UTFPR Curitiba PR Brazil

ISBN: (数字)9798350391084

ISBN: (纸本)9798350391091

This paper presents a new teaching tool with the goal of facilitating the learning of basic programming concepts among high school students and university freshmen. The tool incorporates a user-friendly visual interface and robotics, expanding on the existing Fluxprog project. New features were implemented, such as variables, logical and mathematical operations, conditional structures, and loops, to motivate students to engage in challenges that promote logical thinking. This paper presents the design, implementation and evaluation of a programming teaching tool using a visual language based on flowcharts, with subsequent data collection through a questionnaire and analysis of preliminary results to evaluate the user's experience while interacting with the tool.

关键词： Visualization Flowcharts Conferences Education Data collection User experience Logic Robots programming profession

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Developer-Centric Task Allocation: An Intelligent Approach for Enhanced Software Productivity

Developer-Centric Task Allocation: An Intelligent Approach f...

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International Conference on Information, Intelligence, Systems and Applications (IISA)

作者： Evandro L. R. Fensterseifer Filho Joaquim V. C. Assunção Lisandra M. Fontoura Graduate Program in Computer Science Federal University of Santa Maria - UFSM Santa Maria – RS Brazil Applied Computing Department Federal University of Santa Maria - UFSM Santa Maria – RS Brazil

ISBN: (数字)9798350368833

ISBN: (纸本)9798350368840

Software projects are affected by technical knowledge as well as the personality of the team. Such factors can reduce or increase the software quality and development speed. For successful task allocation, it is essential to consider the skills and profile of each developer, thus maximizing their productivity. In projects with large teams, task allocation can be challenging, and the help of tools can facilitate its execution. In this work, we propose an intelligent approach for allocating software development tasks suitable to the profile of developers. From the literature, we define the appropriate skills and technical profiles for a development team, and the assessment is based on the developer completing a questionnaire. We developed a recommendation system to suggest tasks to be allocated to developers, employing text processing techniques. For validation, 495 tasks were used from an actual project developing simulator software for military training. The recommended allocations were evaluated by the project developers and used to improve the system. Validations showed that the developed approach makes consistent and coherent task recommendations to developers according to the developer profile, as participants agree with the recommendation for 76% of tasks.

关键词： Training Productivity Databases Mathematical models Classification algorithms Resource management Data mining Recommender systems Text processing Software development management

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HistAttentionNAS: A CNN built via NAS for Penile Cancer Diagnosis using Histopathological Images

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Procedia Computer Science 2025年 256卷 764-771页

作者： José Ribamar Durand R. Geraldo Braz Junior Italo Francyles Santos da Silva Rui Miguel Gil da Costa Oliveira Applied Computing Group - Federal University of Maranhão Vila Bacanga São Luís 65080-805 Brazil Graduate Program in Adult Health - PPGSAD - Federal University of Maranhão Vila Bacanga São Luís 65080-805 Brazil

Penile cancer, although rare, has an increasing mortality rate in Brazil, highlighting the need for effective diagnostic methods. Artificial Intelligence (AI) in histopathological analysis can speed up and objectify diagnosis, but designing an ideal architecture is challenging. In this study, we propose a neural architecture search (NAS) methodology for detecting penile cancer in digital histopathology images. We explored different configurations of stem blocks and the inclusion of attention mechanisms, highlighting specific preferences depending on the magnification of the images. The results showed that the NAS methodology enabled the discovery of more accurate and optimized architectures for this task, surpassing conventional models. The proposed models achieve 89.5% and 88.5% F1-Score for 40X and 100X magnification, respectively.

关键词： Penile Cancer Detection Histopathologic Classification Neural Architecture Search

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Broadband Beamforming via Frequency Invariance Transformation and PARAFAC Decomposition for Jamming Mitigation in V2X Scenarios

Broadband Beamforming via Frequency Invariance Transformatio...

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IEEE Conference on Vehicular Technology (VTC)

作者： Daniel V. de Lima João Paulo J. da Costa Antonio Arlis S. da Silva Giovanni A. Santos José Alfredo R. Vargas Auzuir R. de Alexandria Postgraduate Program in Telecommunications Engineering (PPGET) IFCE Fortaleza Brazil Hamm-Lippstadt University of Applied Sciences (HSHL) Lippstadt Germany Graduate School for Applied Research in North Rhine-Westphalia (PKNRW) Bochum Germany Karlsruhe Institute of Technology (KIT) Karlsruhe Gennany Graduate Program in Computing (PPGC) UFRGS Porto Alegre Brazil Department of Electrical Engineering UnB Brasilia Brazil

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

Vehicle-to-Everything (V2X) communication can considerably improve the efficiency and safety of autonomous driving and advanced driver-assistance systems (ADASs). However, V2X communication can be considerably degraded in the presence of cyberattacks, such as radio jamming. Traditionally, beamforming techniques can be applied to increase the signal-to-interference plus noise ratio (SINR). This paper evaluates broadband beamforming in the mmWave spectrum against radio jamming in V2X communication. The exploitation of the mm Wave spectrum in 5G-V2X communication has a natural advantage against radio jamming. First, attenuation is stronger in the mmWave spectrum in the range of 40 GHz or higher than in the traditional 5.9 GHz. Second, to generate broadband radio jamming, the radio jammer requires much more complex hardware and energy consumption. Third, by using broadband beamforming, broadband radio jamming can be considerably attenuated, limiting the degradation of the spectrum by the radio jamming. According to our numerical results, gains of close to 30 dB SINR can be achieved. We propose a broad-band beamforming technique based on the canonical polyadic decomposition via generalized eigenvalue decomposition (CPD-GEVD). The CPD-GEVD broadband beamforming outperforms state-of-the-art beamforming algorithms in most V2X scenarios presented in this paper.

关键词： Vehicular and wireless technologies Array signal processing Interference Attenuation Broadband communication Jamming Millimeter wave communication

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Towards to Beyond 5G Virtual Environment for Cybersecurity Testing in V2X Systems

Towards to Beyond 5G Virtual Environment for Cybersecurity T...

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Workshop on Communication Networks and Power Systems (WCNPS)

作者： Giovanni A. Santos João Paulo J. da Costa Antonio Arlis S. da Silva Hamm-Lippstadt University of Applied Sciences (HSHL) Lippstadt Germany Graduate School for Applied Research in North Rhine-Westphalia (PKNRW) Bochum Germany Karlsruhe Institute of Technology (KIT) Karlsruhe Germany Graduate Program in Computing (PPGC) UFRGS Porto Alegre Brazil

Vehicle-to-Everything (V2X) communication has an essential role for enhancing safety, effi-ciency, and overall driving experience of autonomous driving. To meet the requirements of autonomous driving, V2X demands high data-rate connectivity, per-ceived zero-latency, and high reliability. These char-acteristics are inherently linked to future-generation mobile communication technologies, such as Beyond 5G (B5G) also known as 6G. However, current so-lutions for simulating 5G communications lack inte-gration between the user and control planes, making the simulation not reliable since the 5G control plane functionalities are not taken into account. In this sense, this paper proposes a first contribution towards to the platform called Beyond 5G Virtual Environment for Cy-bersecurity Testing in V2X Systems (B5GCyberTestV2X) by integrating the 5G control plane from Open5GS and UERANSIM into the Simu5G simulator. The inclusion of the control plane in 5G urban mobility simulations increases the security and reliability of the V2X commu-nication service. Therefore, in order to show the impact of the 5G control plane, we validate a simulation with a scenario in the presence of a spoofer transmitting false warning information. As shown in our simulation, the 5G control plane does not allow the connection of the spoofer into 5G network, making the cybersecurity tests of 5G-based V2X communication more realistic.

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Performance analysis of the Raft consensus algorithm on Hyperledger Fabric and Ethereum on cloud

Performance analysis of the Raft consensus algorithm on Hype...

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IEEE International Conference on Cloud computing Technology and Science (CloudCom)

作者： João H. F. Battisti Vitor E. Batista Guilherme P. Koslovski Maurício A. Pillon Charles C. Miers Marco A. Marques Marcos Simplício Diego Kreutz Graduate Program in Applied Computing (PPGCAP) Santa Catarina State University (UDESC) Brazil Laboratory of Computer Networks and Architecture (LARC) Universidade de São Paulo (USP) Brazil Graduate Program in Software Engineering Federal University of Pampa (Unipampa) Brazil

The use of private or consortium blockchains in organizations’ applications is growing. A relevant aspect of blockchains is the choice of consensus mechanism. This decision delimits which blockchain solutions are suitable for the private scenario. Once the consensus mechanism is chosen, more than one blockchain may be enabled. However, this decision making is not trivial and requires detailed experimental indicators about algorithms and blockchains performance. In this context, we provide a comprehensive performance analysis of the Raft consensus mechanism based on its implementation in Hyperledger Fabric and Ethereum blockchain solutions. We performed our experiments on an OpenStack private cloud using each blockchain developer’s default settings for virtual machines. Our findings show how the implementation of each solution can impact the application’s performance under certain conditions.

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Quantum Wrapper Networking: Investigating the Noise Impact of Classical Headers on the Quantum Payload

Quantum Wrapper Networking: Investigating the Noise Impact o...

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2024 Frontiers in Optics, FiO 2024

作者： Gül, Gamze On, Mehmet Berkay Tan, Shannon G. Proietti, Roberto Kanter, Gregory S. Yoo, S.J. Ben Kumar, Prem Graduate Program in Applied Physics Northwestern University 2145 Sheridan Road EvanstonIL60208 United States Department of Electrical and Computer Engineering University of California Davis DavisCA95616 United States Center for Photonic Communication and Computing Dept. of Electrical and Computer Eng. Northwestern University EvanstonIL60208 United States Department of Electronics and Telecommunications Engineering Politecnico di Torino Italy

We study the impact in time and frequency domains of classical headers on quantum payloads in quantum wrapper networking. We identify and characterize in-fiber scattering processes that produce noise photons degrading...

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DeepScheduling: Grid computing Job Scheduler Based on Deep Reinforcement Learning 34th

DeepScheduling: Grid Computing Job Scheduler Based on Deep R...

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34th International Conference on Advanced Information Networking and Applications, AINA 2020

作者： Casagrande, Lucas C. Koslovski, Guilherme P. Miers, Charles C. Pillon, Maurício A. Graduate Program in Applied Computing Santa Catarina State University Joinville Brazil

ISBN: (纸本)9783030440404

Grid systems are large-scale platforms which consume a considerable amount of energy. Several efficient resource/power management strategies were proposed by the specialized literature. However, most of the proposed strategies are rule-based policies which do not exploit workload patterns. Deploying the same set of rules on systems using different usage patterns, and platform settings, may lead to a sub-optimized setup. Due to the complex nature of grid systems, tailoring such a system-specific policy is not a straightforward task. In this paper, we explore a Deep Reinforcement Learning (DRL) method to build an adaptive energy-aware scheduling policy. We trained our algorithm using real workload traces from Grid’5000 platform. Our experiments pointed out an energy setup saving up to 7%, as well as average requests waiting time reduction of 27%. Finally, the resuslts clarify the importance of explore the workload to build system-specific policies. © 2020, Springer Nature Switzerland AG.

关键词： Reinforcement learning

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SSL-C4.5: Implementation of a Classification Algorithm for Semi-supervised Learning Based on C4.5 9th

SSL-C4.5: Implementation of a Classification Algorithm for S...

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9th Brazilian Conference on Intelligent Systems, BRACIS 2020

作者： Ortiz-Díaz, Agustín Alejandro Bayer, Flavio Roberto Baldo, Fabiano Graduate Program in Applied Computing Santa Catarina State University Joinville Brazil

ISBN: (纸本)9783030613792

Classification algorithms have been extensively studied in many of the major scientific investigations in recent decades. Many of these algorithms are designed for supervised learning, which requires labeled instances to achieve effective learning models. However, in many of the real human processes, data labeling is expensive and time-consuming. Because of this, alternative learning paradigms have been proposed to reduce the cost of the labeling process without a significant loss of model performance. This paper presents the Semi-Supervised Learning C4.5 algorithm (SSL-C4.5) designed to work in scenarios where only a small part of the data is labeled. SSL-C4.5 was implemented over the J48 implementation of the C4.5 algorithm available at the WEKA platform. The J48 was modified incorporating a metric for semi-supervised learning. This metric aims at inducing decision tree models able to analyze and extract information from the entire training dataset, including instances of unlabeled data in scenarios where they are the majority. The assessment performed using eight different benchmark datasets showed that the new proposal has achieved promising results compared to the supervised version of C4.5. © 2020, Springer Nature Switzerland AG.

关键词： Decision trees

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Three-Dimensional Wound Reconstruction using Point Descriptors: A Comparative Study

Three-Dimensional Wound Reconstruction using Point Descripto...

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Annual IEEE Symposium on Computer-Based Medical Systems

作者： José R. A. Souto Fellipe M. C. Barbosa Bruno M. Carvalho Graduate Program in Systems and Computing Federal University of Rio Grande do Norte Natal Brazil Department of Informatics and Applied Mathematics Federal University of Rio Grande do Norte Natal Brazil

Precise measurements of chronic wound areas are very important for measuring the efficacy of different treatments, since it provides a clear picture of the chosen treatment evolution. The most used techniques still in use for measuring the wound areas involve manual measuring, paper scales and/or acetate tracing, with each health professional possibly measuring the wounds in slightly different ways, specially when dealing with the wound's depth. Thus, it is common to observe inconsistent wound area estimations by different health workers on the same wounds. Moreover, the measuring process requires direct contact with the wound, increasing the contamination and infection risks, besides possibly causing discomfort for the patient. An alternative that has already been proved successful is the use of Structure from Motion (SfM) to recover three-dimensional representations of a wound's surface and estimate that area. In this work, we analyze the effectiveness of several widely used point descriptors (BRIEF, DRINK, FREAK, ORB, SIFT and SURF) in the SfM wound reconstruction task when applied to real images of realistic synthetic wounds made of latex placed on different areas of the body. The results showed that SIFT, SURF and DRINK produced better area estimates. Since the execution time is a factor, we selected DRINK as the standard descriptor for our system, since it is approximately 9.3 and 4.9 times faster than SIFT and SURF, respectively.

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