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2022 IEEE IFEES World Engineering Education Forum - Global Engineering Deans Council, WEEF-GEDC 2022

作者： Gurganus, Jamie Blorstad, Richard Headley, Makayla Engineering and Computing Education Program and Mechanical Engineering and Graduate School University of Maryland Baltimore County Baltimore United States Mechanical Engineering University of Maryland Baltimore County Baltimore United States Chemical Engineering University of Maryland Baltimore County Baltimore United States

ISBN: (纸本)9781665475280

In an effort to continue to help provide various and thriving experiences to engineering undergraduates and help increase retention, a mid-size university uses a high impact practice of using peer teachers in the classroom. It is a standard practice to use graduate teaching assistants in most areas of higher education, especially in engineering classes, discussions, labs or just to hold office hours and grade. However, an increasing number of universities have adopted and leveraged undergraduate teaching assistants as it demonstrates to effectively improve students' grades, retention, student self-efficacy, and provide some financial relief to academic institutions [1]. The impact of using peer teachers is especially evident in the first and second years in engineering. Students who participate in the role are third year or above demonstrate expertise, leadership, and an interest in teaching as part of their development. At a mid-size minority serving institution, an undergraduate teaching assistant (termed as teaching fellow) was developed informally in 2005 in the mechanical and chemical engineering department and expanded in 2017 to the entire College of Engineering and Informational Technology. In this case study, alumni and current teaching fellows were interviewed to assess the impact of their experiences and how it influenced their educational experience in their major and current career. Several themes were discovered to include increased professional and personal skill sets, self-efficacy in engineering, motivation to participate in the program, impact on career, creation of community and improvements needed to the program. A few teaching fellows decided to continue to be a p12 teacher. © 2022 IEEE.

关键词： Students

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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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Local Periodicity-Based Beat Tracking for Expressive Classical Piano Music

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IEEE/ACM Transactions on Audio Speech and Language Processing 2023年 31卷 2824-2835页

作者： Chiu, Ching-Yu Muller, Meinard Davies, Matthew E. P. Su, Alvin Wen-Yu Yang, Yi-Hsuan National Cheng Kung University Academia Sinica Graduate Program of Multimedia Systems and Intelligent Computing Tainan701401 Taiwan International Audio Laboratories Erlangen Erlangen91058 Germany University of Coimbra Department of Informatics Engineering Centre for Informatics and Systems Coimbra3004-531 Portugal National Cheng Kung University Department of Computer Science and Information Engineering Tainan701401 Taiwan Taiwan AI Labs Yating Music Team Taipei103622 Taiwan Academia Sinica Research Center for IT Innovation Taipei11529 Taiwan

To model the periodicity of beats, state-of-the-art beat tracking systems use 'post-processing trackers' (PPTs) that rely on several empirically determined global assumptions for tempo transition, which work well for music with a steady tempo. For expressive classical music, however, these assumptions can be too rigid. With two large datasets of Western classical piano music, namely the Aligned Scores and Performances (ASAP) dataset and a dataset of Chopin's Mazurkas (Maz-5), we report on experiments showing the failure of existing PPTs to cope with local tempo changes, thus calling for new methods. In this paper, we propose a new local periodicity-based PPT, called predominant local pulse-based dynamic programming (PLPDP) tracking, that allows for more flexible tempo transitions. Specifically, the new PPT incorporates a method called 'predominant local pulses' (PLP) in combination with a dynamic programming (DP) component to jointly consider the locally detected periodicity and beat activation strength at each time instant. Accordingly, PLPDP accounts for the local periodicity, rather than relying on a global tempo assumption. Compared to existing PPTs, PLPDP particularly enhances the recall values at the cost of a lower precision, resulting in an overall improvement of F1-score for beat tracking in ASAP (from 0.473 to 0.493) and Maz-5 (from 0.595 to 0.838). © 2014 IEEE.

关键词： Hidden Markov models

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An UHD 4K@120fps Hardware for the VVC Prediction Refinement with Optical Flow

An UHD 4K@120fps Hardware for the VVC Prediction Refinement ...

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Symposium on Integrated Circuits and Systems Design (SBCCI)

作者： Murilo Perleberg Marcello M. Muñoz Denis Maass Vladimir Afonso Luciano Agostini Marcelo Porto Video Technology Research Group Graduate Program in Computing Federal University of Pelotas Pelotas Brazil Video Technology Research Group Sul-rio-grandense Federal Institute Pelotas Brazil

This work presents a hardware design for the Prediction Refinement with Optical Flow (PROF). The PROF is incorporated in the Affine Prediction from the recent Versatile Video Coding (VVC) standard. While the Affine Prediction allows the representation of complex motions in the scene as rotation, zoom, and skew, by performing motion compensation over each subblock of the current block, the PROF enhances video encoding efficiency by generating the virtual rotation of each subblock. However, the PROF supports a wide range of block sizes, demanding intensive computational processing and therefore requiring dedicated hardware design. So, the PROF Hardware Design was developed to process one 4×4 subblock at each clock cycle. The synthesis results for TSMC 40nm technology show that the PROF Hardware Design can process 120 frames per second of Ultra-High Definition UHD 4K resolution videos while dissipating 0.98mW when operating at 62MHz. This work also presents integration between the PROF Hardware Design and an Affine Motion Compensation hardware. To the best of author's knowledge, this is the first hardware design for PROF processing in the literature.

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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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Monitoring Metrics for Load Balancing over Video Content Distribution Servers

Monitoring Metrics for Load Balancing over Video Content Dis...

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International Conference on Network and Service Management

作者： Edenilson Jônatas dos Passos Adriano Fiorese Graduate Program in Apllied Computing (PPGCAP) Department of Computer Science (DCC) Santa Catarina State University (UDESC) Joinville Brazil

ISBN: (数字)9783903176515

ISBN: (纸本)9781665489928

Cloud computing and video streaming services have been in constant expansion in recent years. Along with it, the demand for computing resources has also increased significantly. In this context, monitoring the use of these resources is crucial to maintain a satisfactory level of Quality of Service and, consequently, Quality of Experience, especially in video transmission services. This work discusses a new method of monitoring resources and quality of service metrics on content servers involving CPU utilization and server throughput, which is obtained in a distributed way. For that, a distributed collector system that is based on a modified version of the ring election algorithm is developed to retrieve the Quality of Service metrics in each server. Evaluation experiment results show that there are no performance gains on the system such as the content loading faster for the user, there are however, improvements in terms of the whole system scalability. The greater the number of servers for monitoring, the better the approach is compared to the traditional method of monitoring resources through request and response.

关键词： Measurement Voting Scalability Quality of service Streaming media Performance gain Throughput

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Energy-Efficient Haar Transform Architectures Using Efficient Addition Schemes 11

Energy-Efficient Haar Transform Architectures Using Efficien...

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11th IEEE Latin American Symposium on Circuits and Systems, LASCAS 2020

作者： Seidel, Henrique Rosa, Morgana Da Paim, Guilherme Costa, Eduardo Da Almeida, Sergio Bampi, Sergio Graduate Program on Electronic Engineering and Computing Pelotas Brazil Porto Alegre Brazil

ISBN: (纸本)9781728134277

This paper proposes an energy-efficient Haar transform architecture using efficient adders circuits. Nine levels of decomposition in a fixed-point format are used in the architectures. The Processing Module (PM) of Haar explores efficient Parallel Prefix Adders (PPA) and adder compressors. Since power-of-two operations approximate one of the inputs of the PM module, efficient adder compressors implement them. It allowed a very high precision adjustment of the transform coefficient. The architectures were described in VHDL and synthesized using the ST 65nm CMOS cell library. The results show that the Haar architecture with 4-2 and 5-2 compressors in the PM module, both with the recombination line with a Sklansky (SK) adder, is more efficient. It has a reduction of 33.6% in the cell area, with a 35.7% reduction in total power in comparison with the literature. Moreover, the Haar architecture with 4-2 and 5-2 adder compressors is more efficient than the one using the tool-selected implementation of the behavioral "+" (sum) in VHDL, with 23% of power reduction. © 2020 IEEE.

关键词： Adders

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Fine Tuning Swin Transformer Based Pretrained Model for Microscopic Fungi Images Classification

Fine Tuning Swin Transformer Based Pretrained Model for Micr...

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International Conference on Computer, Control, Informatics and Its Applications (IC3INA)

作者： Muchamad Galih Anggara Ahmad Hindasyah Julfa Muhammad Amda A. A. Waskita Graduate Program of Informatics Engineering Universitas Pamulang Pamulang Indonesia Research Center for Computing National Research and Innovation Agency Cibinong Indonesia Research Center for Data and Information Sciences National Research and Innovation Agency Bandung Indonesia

ISBN: (数字)9798331542313

ISBN: (纸本)9798331542320

Accurate image classification of microscopic pathogenic fungi is a challenge in the field of mycology. This study proposes a Swin Transformer-based approach to automate the identification process. The Swin Transformer model, known for its ability to capture complex spatial interactions, is adapted from ImageNet’s pre-trained model. The adaptation process is focused on fine-tuning the final classification layer to match the 5 classes of fungi present in the dataset. This classification layer is replaced by a new fully-connected layer that has the number of output according to the number of fungal classes. The weights on these layers are randomly initialized and trained in conjunction with several final convolutional layers. In addition, regularization techniques such as dropouts are applied to prevent overfitting. Adaptation using 5-fold cross-validation was carried out to find the best fold with hyperparameters including AdamW optimizer with a learning rate of 10 −3 , StepLR scheduler, batch size of 128 images, and a total of 9 epochs / iterations. After that, fine-tuning is carried out on the best fold with hyperparameters including AdamW optimizer with a learning rate of 10 −5 , StepLR scheduler, batch size of 32 images, and a total of 100 epochs / iterations. The final result showed excellent performance, with accuracy, precision, recall and an average F1-score of 95%. These results show the potential of the Swin Transformer in becoming an effective tool for mycologists.

关键词： Fungi Training Digital control Adaptation models Accuracy Microscopy Transformers Informatics Tuning Image classification

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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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Machine Learning for Prediction of Adverse Drug Reactions: Application to Neonatal Intensive Care Units

SSRN

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SSRN 2023年

作者： do Nascimento, Amanda Roseane Farias Greati, Victor Rodrigues Leopoldino, Ramon Weyler Duarte Martins, Rand Randall Barbosa, Euzebio Guimarães Graduate Program in Pharmaceutical Sciences Health Sciences Center Federal University of Rio Grande do Norte Rio Grande do Norte Natal Brazil Graduate Program in Systems and Computing Department of Informatics and Applied Mathematics Federal University of Rio Grande do Norte Rio Grande do Norte Natal Brazil

Introduction: The intensive care of neonates is associated with the entry of a large volume of data in their medical records. The treatment of this data can be done through Machine Learning: a tool capable of assisting in the detection and decision-making of a wide range of medical conditions, including adverse drug reactions (ADR). Objective: To train a prediction model to assist in the detection of adverse drug reactions in neonates admitted to an ICU. Methodology: observational study developed in the Neonatal Intensive Care Unit of a teaching hospital in Brazil. Clinical data were collected from daily pharmacotherapeutic follow-up, processed and analyzed by machine learning through libraries written in Python language. Results: Eight hundred and three neonates were included in the study, with a mean gestational age of 32.2 ± 4.2 weeks and mean birth weight of 1807.2 ± 936.6g. The incidence of ADR was 10.8%. Antimicrobials, especially aminoglycosides, were the most prescribed drugs for this population. An algorithm was trained and tested to predict ADR in the NICU, in which the metrics were precision (0.35) and recall (0.823), with specificity (80%) and sensitivity (67%). Conclusion: There is a high potential in the machine learning method to predict ADR in neonates admitted to an ICU. © 2023, The Authors. All rights reserved.

关键词： Forecasting

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