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IEEE Latin American Robotics Symposium, LARS

作者： Gabriel Batistuta Urbano Lopes Sebastião Emidio Alves Filho B.Sc. in Computer Science Rio Grande do Norte State University Mossoró RN Brazil Graduate Program in Computer Science - PPgCC UERN/UFERSA Rio Grande do Norte State University (UERN) and Federal University of the Semi-Arid Region (UFERSA) Mossoró RN Brazil

Educational robotics awakens students’ interest and curiosity in the world of technology and innovation. By exploring concepts in programming, electronics, and mechanics, students are introduced to subject areas allowing students of all ages to develop science, technology, engineering, and math (STEM) skills. However, getting information on this topic has become difficult since, with the growth of internet content production, learning objects have become constantly dispersed. Thus, the present work has the general objective of presenting the strategy used to search and ingest data from learning objects in an automated way in RepositORE, a repository where these objects of Education and robotics can be stored and searched by users who need to acquire certain skills. For the objects of Educational Robotics to be found more quickly and accurately, the software uses data extraction techniques to search for data from the learning objects on the web. Robot Finder optimizes the search and recording of information from internet object data so that it can be accessed by RepositORE.

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Low-Energy and Reduced-Area Hardware Architecture for the Versatile Video Coding FME

Low-Energy and Reduced-Area Hardware Architecture for the Ve...

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Symposium on Integrated Circuits and Systems Design (SBCCI)

作者： Vanio Rodrigues Filho Ismael Seidel Nicole Citadin Marcio Monteiro Mateus Grellert José Luis Güntzel Dept. of Informatics and Statistics (INE) Embedded Computing Lab. (ECL) Computer Science Graduate Program (PPGCC) Federal University of Santa Catarina (UFSC) Florianópolis Brazil Federal University of Rio Grande do Sul (UFRGS) Porto Alegre Brazil

The Versatile Video Coding (VVC) standard incorporates a number of new tools to significantly improve the coding efficiency over its predecessor, the High Efficiency Video Coding (HEVC), at the cost of increased complexity. Hence, to meet real-time and energy efficiency requirements, portable devices must adopt techniques to effectively reduce the VVC complexity, including customized hardware accelerators. This work presents a low-energy hardware architecture design for the Fractional Motion Estimation (FME), which is amongst the most critical VVC and HEVC encoder steps. The proposed architecture reduces the FME complexity by operating over a subset of candidates selected to reduce the energy and area demands without compromising too much the coding efficiency. At the average BD-Rate of only 0.34% and 0.28% for the Low Delay with P slices only (LD-P) and Random Access (RA) configurations, respectively, the proposed architecture achieves a 75.5% energy consumption reduction when compared to a baseline FME architecture while occupying only 36.6 % of the area.

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Hop-by-hop Accounting and Rewards for Packet dIspAtching

Hop-by-hop Accounting and Rewards for Packet dIspAtching

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Caciano Machado Renan R. S. dos Santos Carla Merkle Westphall Graduate Program in Computer Science - PPGCC Network and Management Laboratory - LRG Federal University of Santa Catarina - UFSC Florianópolis Brazil

ISBN: (纸本)9781665416597

Community networks are prone to free-riders, i.e., participants who take advantage of cooperation from others' routers but do not contribute reciprocally. In this paper, we present HARPIA, a system for credit-based incentive mechanisms for data forwarding in community networks aimed to prevent selfish behavior. HARPIA does not require a trusted third-party or tamper-resistant security modules as in other incentive mechanisms. Instead, it uses a distributed accounting scheme (DPIFA) to estimate the balance of data forwarding contribution and consumption of each network router and settle correspondent cryptocurrency debts on an Ethereum smart contract. On-chain settlement transactions are performed every HARPIA cycle (e.g., daily, weekly, monthly) and must be validated by at least m-of-n network routers using a multi-signature scheme (MuSig). We also realized a performance evaluation, security threat assessment, and cryptocurrency costs estimation. Results show that our proposal is suitable for community networks with up to 64 infrastructure routers under specific m-of-n MuSig thresholds.

关键词： Performance evaluation Privacy Smart contracts Estimation Distributed databases Threat assessment Dispatching

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Vehicular Perception Based on Inertial Sensing: A Structured Mapping of Approaches and Methods

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SN computer science 2020年第5期1卷 1-24页

作者： Menegazzo, Jeferson von Wangenheim, Aldo Graduate Program in Computer Science (PPGCC) Image Processing and Computer Graphics Lab (LAPIX) Department of Informatics and Statistics (INE) Brazilian Institute for Digital Convergence (INCoD) Federal University of Santa Catarina (UFSC) Santa Catarina Florianópolis Brazil

In this paper, we present a structured literature mapping of the state-of-the-art of vehicular perception methods and approaches using inertial sensors. An in-depth investigation and classification were performed employing the results of a systematic literature review. The analysis focused on identifying methods that capture signals provided by inertial sensors such as accelerometers and gyroscopes to recognize transient or persistent events associated with the vehicle’s movement. We classified these events into vehicular exteroception, associated with potholes, cracks, speed bumps, pavement type, conservation state;and vehicular proprioception, associated with lane change, braking, skidding, aquaplaning, turning right or left. Through the comprehensive study of publications in a 7-year time window, in addition to the methods, we have also identified their dependency factors, hardware platforms and applications. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Driver behaviour Driving style Inertial sensors Intelligent transport systems Road conditions Road surface anomaly

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An Analysis of a Gamification Application for Teaching and Learning Knowledge Management under the Qualitative and Affective Computing Contexts

An Analysis of a Gamification Application for Teaching and L...

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Frontiers in Education (FIE) Conference

作者： Igor Ernesto Ferreira Costa Antonilson da Silva Alcantara Elisiane Monteiro Soares Raimundo Viégas Junior Elziane Monteiro Soares Emanuel Amoras Rodrigues Sandro Ronaldo Bezerra Oliveira Graduate Program in Computer Sciense (PPGCC) Institute of Exact and Natural Sciences (ICEN) Federal University of Pará (UFPA) Belém PA Brazil Departament of Teaching Federal Institute of Education Science and Technology of Maranhão (IFMA) Viana MA Brazil Faculty of Computing Institute of Exact and Natural Sciences (ICEN) Federal University of Pará (UFPA) Belém PA Brazil

Knowledge as an organizational asset is of great importance in the decision-making process, which needs to be managed properly in order to achieve, from this, a competitive differential in the respective segment of the labor market where the organization is inserted. Thus, managing knowledge and making it an exploitable asset within the organization is of great importance. Therefore, generating new ideas, solutions and innovations and making them available to members of the organization, whether from an organizational database or even from the process of socialization among employees, is necessary for the maintenance of knowledge. Based on this, this work presents the evaluation of an experience report that consisted of the application of a gamification to support the teaching and learning of the knowledge management assets and process in a research laboratory context. The data collected, both during the application of gamification, and at the end of the experiment in the feedback and evaluation meeting, were analyzed qualitatively from the application of Affective Computing and also through the SWOT (Strengths, Weaknesses, Opportunities, Threats) matrix. After analyzing these data, the effectiveness of this gamified proposal was proven in order to stimulate the teaching and learning process of knowledge management.

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Complexity and Coding Efficiency Assessment of the Versatile Video Coding Standard

Complexity and Coding Efficiency Assessment of the Versatile...

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IEEE International Symposium on Circuits and Systems

作者： Icaro Siqueira Guilherme Correa Mateus Grellert Catholic University of Pelotas Pelotas Brazil Video Technology Research Group Federal University of Pelotas Pelotas Brazil Graduate Program in Computer Science (PPGCC) Federal University of Santa Catarina Florianopolis Brazil

ISBN: (纸本)9781728192017;9781728192024

The Versatile Video Coding standard was finalized by the Joint Video Exploration Team in July 2020 and is currently considered the state-of-the-art video compression technology. VVC significantly improves coding efficiency compared to HEVC thanks to several new features and tools that incur a large increase in computational cost. This paper presents a complexity and coding efficiency assessment of VVC divided into three analyses, focusing on: (1) the impact of using SIMD optimizations in the VVC Test Model software, (2) the impact of limiting partitioning structures when encoding, and (3) the computational cost associated to each encoding tool in VVC. Experimental results show that SIMD optimizations accelerate the encoding time by 40%, on average, and that limiting the available partitioning structures can decrease encoding time between 40% and 73%. The software profiling revealed that inter-frame prediction is responsible for almost half of the total encoding time. Finally, the paper also presents an analytic discussion on tools and partitioning possibilities that are rarely chosen in the mode decision process despite their high impact in coding complexity.

关键词： Limiting Tools Encoding Software Complexity theory Standards Optimization

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Standalone Rate-Distortion FME Architecture

Standalone Rate-Distortion FME Architecture

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Symposium on Integrated Circuits and Systems Design (SBCCI)

作者： Vanio Rodrigues Filho Marcio Monteiro Ismael Seidel Mateus Grellert José Luís Güntzel Embedded Computing Lab. (ECL) Computer Science Graduate Program (PPGCC) Federal University of Santa Catarina (UFSC) Florianópolis Brazil

ISBN: (数字)9781728196251

ISBN: (纸本)9781728196268

The high computing complexity of video encoders hinders real-time throughput using a software implementation. This drawback can be amended by implementing hardware accelerators for the most computing-intensive encoder tools to achieve real-time processing while maintaining energy efficiency. In this work, we investigate how approximate and precise versions of the Fractional Motion Estimation (FME), one of the most demanding steps of modern encoders, compare in terms of hardware resources and coding efficiency. To that purpose, we designed a hardware architecture for FME targeting real-time throughput for High Definition (HD) sequences and beyond. This architecture searches over 48 candidates and, unlike most related work, uses 16-bit wide horizontal interpolated samples, which is in compliance with the High Efficiency Video Coding (HEVC) reference software. Also, it integrates all FME required computations, including the rate cost of each candidate block, what makes it a standalone FME accelerator. We analyze the impacts of our design choices in terms of coding efficiency, using software simulations, and in terms of power/energy consumption, using results obtained from standard cell synthesis.

关键词： computer architecture Interpolation Encoding Hardware Software Standards Distortion

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Multi-Contextual and Multi-Aspect Analysis for Road Surface Type Classification Through Inertial Sensors and Deep Learning

Multi-Contextual and Multi-Aspect Analysis for Road Surface ...

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Brazilian Symposium on Computing System Engineering (SBESC)

作者： Jeferson Menegazzo Aldo von Wangenheim Graduate Program in Computer Science (PPGCC) Image Processing and Computer Graphics Lab (LAPIX) Brazilian National Institute for Digital Convergence (INCoD) Federal University of Santa Catarina (UFSC) Florianópolis Brazil

ISBN: (数字)9781728182865

ISBN: (纸本)9781728182872

The demand for a variety of situational data from the traffic environment and its participants has intensified with the development of applications in Intelligent Transport Systems (ITS). Among these data, the road surface type classification is one of the most important and can be used in the entire ITS domain. For its widespread application, it is necessary to employ a robust technology for the generation of raw data and to develop of a reliable and stable model to process these data in order to produce the classification. The developed model must operate correctly in different vehicles, under different driving styles and in different environments in which a vehicle can travel. In this work we employ inertial sensors, represented by accelerometers and gyroscopes, which are a safe, non-polluting, and low-cost alternative, ideal for large-scale use. We collect nine datasets with contextual variations, including three different vehicles, with three different drivers, in three different environments, in which there are three different road surface types, in addition to variations in the conservation state and presence of anomalies and obstacles such as potholes and speed bumps. After data collection, these data were used in experiments to evaluate various aspects, such as the influence of the vehicle data collection point, the analysis domain, the model input features, and the data window. Afterwards we evaluated the learning and generalization capacity of the models for unknown contexts. In a third step, the data were used in three Deep Neural Network (DNN) models: LSTM-based, GRU-based, and CNN-based. Through a multi-aspect and multi-contextual analysis, we considered the CNN-based model as the best one, which obtained an average accuracy between the data collection placements of 94.27% for learning and 92.70% for validation, classifying the road surface between asphalt, cobblestone or dirt road segments.

关键词： Roads Sensors Data models Vehicles Sensor systems Deep learning Data collection

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Case-based cybersecurity incident resolution 31

Case-based cybersecurity incident resolution

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31st International Conference on Software Engineering and Knowledge Engineering, SEKE 2019

作者： Colome, Marcelo Nunes, Raul Ceretta De Lima Silva, Luis Alvaro Graduate Program in Computer Science - PPGCC Applied Computing Department Federal University of Santa Maria Av. Roraima Campus UFSM Camobi Santa Maria RS Brazil

ISBN: (纸本)1891706489

Intelligent computing techniques have a paramount importance to the treatment of cybersecurity incidents. In such Artificial Intelligence (AI) context, while most of the algorithms explored in the cybersecurity domain aim to present solutions to intrusion detection problems, these algorithms seldom approach the correction procedures that are explored in the resolution of cybersecurity incident problems that already took place. In practice, knowledge regarding cybersecurity resolution data and procedures is being under-used in the development of intelligent cybersecurity systems, sometimes even lost and not used at all. In this context, this work proposes to integrate Case-Based Reasoning techniques and IODEF standard in order to retain concrete problem-solving experiences of cybersecurity incident resolution to be reused in the resolution of new incidents. Experimental results so far obtained with a Case-based Cybersecurity Incident Resolution System (CbCSecIRS) implemented show that information security knowledge can be retained in a reusable memory, so improving the resolution of new cybersecurity problems. © 2019 Knowledge Systems Institute graduate School. All rights reserved.

关键词： Intrusion detection

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IORE: A Flexible and Distributed I/O Performance Evaluation Tool for Hyperscale Storage Systems

IORE: A Flexible and Distributed I/O Performance Evaluation ...

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IEEE Symposium on computers and Communications

作者： Eduardo C. Inacio Mario A. R. Dantas Graduate Program in Computer Science (PPGCC) Federal University of Santa Catarina (UFSC) Florianópolis Brazil

The process of input/output (I/O) performance evaluation, when considering the emerging data-intensive applications, over modern multilayered parallel I/O software stacks and hyperscale storage systems, has proved to be a complex effort. Although the importance of this subject and existence of common tools, the I/O research community still searching for a unified, user-friendly, and flexible approach to tackle the well-known issues. Proposals such as application I/O kernels and synthetic I/O workload generators have been widely adopted for the I/O performance evaluation. Toward to meet a differentiated mechanism, we propose the IOR-EXTENDED (IORE) research work, which can be considered as an enhanced I/O performance evaluation tool. More than a synthetic workload generator, IORE was designed to reduce the time to response in I/O performance experiments and analysis. In this paper, we present the IORE environment, which indicates from the first results a promising experimental tool.

关键词： Generators Tools Performance evaluation Benchmark testing Kernel computers

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