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Estimating computer systems power consumption for time scaled experiments

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Estimating computer systems power consumption for time scale...

IEEE International Conference on Consumer Electronics - Berlin (ICCE-Berlin)

作者： Bruna M. J. C. Vilar Arthur V. De N. Silva Igor M. B. Ulisses Jaidilson Jó da Silva Angelo Perkusich Fred M. Bublitz Post-Graduate Program in Electrical Engineering - PPgEE - COPELE Embedded Systems and Pervasive Computing Laboratory Center of Electrical Engineering and Informatics Universidade Federal de Campina Grande Campina Grande PB BR Embedded Systems and Pervasive Computing Laboratory Center of Electrical Engineering and Informatics Federal University of Campina Grande Campina Grande Brazil Electrical Engineering Department (DEE) Paraíba State University (UEPB)

Power policies are widely applied at computer systems such as desktops, notebooks, and smartphones. For example, the timeout policy to turn off the display. A power management policy must be tested by measuring the power consumption of an experimental platform running workloads. Such tests must be fast enough to feedback the development process with valuable information about the power policy performance. In this work, we have presented a methodology for estimating computer systems power consumption for time scaled experiments. Using the proposed methodology, we have reduced the overall time for developing a power management policy.

关键词： Computers Power demand Switches Power measurement Indexes Time measurement Turning

An Analysis Method for Metric-Level Switching in Beat Tracking

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arXiv 2022年

作者： Chiu, Ching-Yu Müller, Meinard Davies, Matthew E.P. Su, Alvin Wen-Yu Yang, Yi-Hsuan Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University Academia Sinica Taiwan International Audio Laboratories Erlangen Germany Department of Informatics Engineering Centre for Informatics and Systems The University of Coimbra University of Coimbra Portugal Department of CSIE National Cheng Kung University Taiwan Taiwan AI Labs Taiwan

For expressive music, the tempo may change over time, posing challenges to tracking the beats by an automatic model. The model may first tap to the correct tempo, but then may fail to adapt to a tempo change, or switch between several incorrect but perceptually plausible ones (e.g., half- or double-tempo). Existing evaluation metrics for beat tracking do not reflect such behaviors, as they typically assume a fixed relationship between the reference beats and estimated beats. In this paper, we propose a new performance analysis method, called annotation coverage ratio (ACR), that accounts for a variety of possible metric-level switching behaviors of beat trackers. The idea is to derive sequences of modified reference beats of all metrical levels for every two consecutive reference beats, and compare every sequence of modified reference beats to the subsequences of estimated beats. We show via experiments on three datasets of different genres the usefulness of ACR when being utilized alongside existing metrics, and discuss the new insights that can be gained. © 2022, CC BY.

关键词： Taps

Confidentialchain: Enhancing the Security of a Notarization System Based on Blockchain

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Confidentialchain: Enhancing the Security of a Notarization ...

Blockchain computing and Applications (BCCA), International Conference on

作者： Magno Alves Cavalcante José Antônio Moreira Xexéo Anderson Fernandes Pereira Dos Santos Information Technology and Telecommunications Petróleo Brasileiro S.A. – PETROBRAS Rio de Janeiro Brazil Graduate Program in Systems and Computing Instituto Militar de Engenharia Rio de Janeiro Brazil Venturus Centro de Inovação Tecnológica Brazil

ISBN: (数字)9798350351538

ISBN: (纸本)9798350351545

Blockchain platforms offer traceability, integrity, immutability and availability by native construction, providing resources for auditors to attest the security and temporal traceability of data stored in the blocks. However, permissioned blockchain platforms do not provide confidentiality and nonrepudiation natively for sensitive user data. In this work, Confidentialchain is proposed as a hybrid cryptography schema to aggregate the characteristics of confidentiality and non-repudiation to a confidential data storage system, which is executed on a permissioned blockchain network, ensuring sufficient confidence for the subsequent secure use of sensitive user data that are stored on the blockchain network.

关键词： Buffer storage Aggregates Non-repudiation Blockchains

Source separation-based data augmentation for improved joint beat and downbeat tracking

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arXiv 2021年

作者： Chiu, Ching-Yu Ching, Joann Hsiao, Wen-Yi Chen, Yu-Hua Su, Alvin Wen-Yu Yang, Yi-Hsuan Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University Academia Sinica Taiwan Research Center for IT Innovation Academia Sinica Taiwan Yating Music Team Taiwan AI Labs Taiwan Dept. CSIE National Cheng Kung University

Due to advances in deep learning, the performance of automatic beat and downbeat tracking in musical audio signals has seen great improvement in recent years. In training such deep learning based models, data augmentation has been found an important technique. However, existing data augmentation methods for this task mainly target at balancing the distribution of the training data with respect to their tempo. In this paper, we investigate another approach for data augmentation, to account for the composition of the training data in terms of the percussive and non-percussive sound sources. Specifically, we propose to employ a blind drum separation model to segregate the drum and non-drum sounds from each training audio signal, filtering out training signals that are drumless, and then use the obtained drum and non-drum stems to augment the training data. We report experiments on four completely unseen test sets, validating the effectiveness of the proposed method, and accordingly the importance of drum sound composition in the training data for beat and downbeat tracking. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep learning

An Anisotropic Diffusion Filtering Implementation to Execute in Parallel Distributed systems

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An Anisotropic Diffusion Filtering Implementation to Execute...

IEEE International Conference on Computational Science and Engineering Workshops (CSEWORKSHOPS)

作者： Antonio C. Sobieranski Leandro Coser M.A.R. Dantas Aldo v. Wangenheim Eros Comunello LAPIX-Image Processing and Graphic Computing Lab The Cyclops Group Federal University of Santa Catarina Brazil LaPeSD-Distributed Systems Research Laboratory Federal University of Santa Catarina Brazil Graduate Program Applied Computer Science Universidade do Vale do Itajaí Brazil

ISBN: (纸本)9780769532578

In this paper, we present a parallelization of a filtering algorithm related to non-linear anisotropic diffusion, used to enhance the performance of an application in a parallel distributed system. The anisotropic diffusion is a well-established technique for image enhancement by means of diffusivity functions, which act as border attenuators. However, it requires a high computational cost when a large amount of data is used. The proposed implementation was parallelized considering both point-to-point and collective communications, adopting the MPI paradigm. Results from both approaches indicate that the proposed algorithm has reached interesting levels of performance (81% and 93% of efficiency, respectively) when compared to the execution of one process in a single computer node. In addition, our results indicate an enhancement of around 21% utilizing the collective communication strategy when compared to point-to-point communication.

关键词： Filtering Magnetic separation Anisotropic magnetoresistance Ions Kernel Maximum likelihood detection Pixel

Content-Based Echo Chamber Detection on Social Media Platforms

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Content-Based Echo Chamber Detection on Social Media Platfor...

International Conference on Advances in Social Network Analysis and Mining, ASONAM

作者： Fernando H. Calderón Li-Kai Cheng Ming-Jen Lin Yen-Hao Huang Yi-Shin Chen Social Networks and Human Centered Computing Taiwan International Graduate Program Institute of Information Science Academia Sinica Hsinchu City Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu City Taiwan National Taiwan University Taipei City Taiwan

ISBN: (数字)9781450368681

ISBN: (纸本)9781450368681

“Echo chamber” is a metaphorical description of a situation in which beliefs are amplified inside a closed network, and social media platforms provide an environment that is well-suited to this phenomenon. Depending on the scale of the echo chamber, a user's judgment of different opinions may be restricted. The current study focuses on detecting echoing interaction between a post and its related comments to then quantify the predominating degree of echo chamber behavior on Facebook pages. To enable such detection, two content-based features are designed; the first aids stance representation of comments on a particular discussion topic, and the second focuses on the type and intensity of emotion elicited by a subject. This work also introduces data-driven semi-supervised approaches to extract such features from social media data.

关键词： Feature extraction Social network services Fans Urban areas Connectors Linguistics Computational modeling

Mixing-specific data augmentation techniques for improved blind violin/piano source separation

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arXiv 2020年

作者： Chiu, Ching-Yu Hsiao, Wen-Yi Yeh, Yin-Cheng Yang, Yi-Hsuan Su, Alvin Wen-Yu Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University and Academia Sinica Taiwan Yating Music Team Taiwan AI Labs Taiwan Research Center for IT Innovation Academia Sinica Taiwan Dept. CSIE National Cheng Kung University Taiwan

Blind music source separation has been a popular and active subject of research in both the music information retrieval and signal processing communities. To counter the lack of available multi-track data for supervised model training, a data augmentation method that creates artificial mixtures by combining tracks from different songs has been shown useful in recent works. Following this light, we examine further in this paper extended data augmentation methods that consider more sophisticated mixing settings employed in the modern music production routine, the relationship between the tracks to be combined, and factors of silence. As a case study, we consider the separation of violin and piano tracks in a violin piano ensemble, evaluating the performance in terms of common metrics, namely SDR, SIR, and SAR. In addition to examining the effectiveness of these new data augmentation methods, we also study the influence of the amount of training data. Our evaluation shows that the proposed mixing-specific data augmentation methods can help improve the performance of a deep learning-based model for source separation, especially in the case of small training data. Copyright © 2020, The Authors. All rights reserved.

关键词： Mixing

Opportunities and Challenges from Using Animal Videos in Reinforcement Learning for Navigation

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arXiv 2022年

作者： Giammarino, Vittorio Queeney, James Carstensen, Lucas C. Hasselmo, Michael E. Paschalidis, Ioannis Ch. Division of Systems Engineering Boston University BostonMA02215 United States Center for Systems Neuroscience Department of Psychological and Brain Sciences Graduate Program for Neuroscience Boston University BostonMA02215 United States College of Engineering Hariri Institute for Computing & Computational Science & Engineering Boston University BostonMA02215 United States

We investigate the use of animal videos (observations) to improve Reinforcement Learning (RL) efficiency and performance in navigation tasks with sparse rewards. Motivated by theoretical considerations, we make use of weighted policy optimization for off-policy RL and describe the main challenges when learning from animal videos. We propose solutions and test our ideas on a series of 2D navigation tasks. We show how our methods can leverage animal videos to improve performance over RL algorithms that do not leverage such observations. © 2022, CC BY.

关键词： Reinforcement learning

Multilabel Machine Learning System for Predicting Gender and Age Group from Human Gait

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Multilabel Machine Learning System for Predicting Gender and...

Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Nunung Nurul Qomariyah Ahmad Nurul Fajar Vincent Yono Dimitar Kazakov Computer Science Department School of Computing and Creative Arts Bina Nusantara University Jakarta Indonesia Information Systems Management Department Binus Graduate Program Master of Information System Management Bina Nusantara University Jakarta Indonesia Computer Science Department University of York United Kingdom

This paper explores the development of a multilabel machine learning system for predicting both gender and age from human gait patterns. Gait analysis, a non-intrusive method of identifying subtle nuances in human movement, has proven to be a rich source of information related to demographic characteristics. The research extends beyond traditional single-label classification approaches, adopting a multilabel framework to simultaneously predict gender and age *** study evaluates various multilabel machine learning algorithms, with the Random k-labeLsets (RAKEL) algorithm demonstrating superior performance in predicting gender and age labels from human gait datasets. The accuracy of the algorithm can reach up to 87%. We also compared the multilabel approach with several multiclass algorithms such as Decision Tree, Random Forest, Gradient Boosting, K-Neighbors and XGBoost. However, when we also considering the training time, Classifier Chain algorithm showed the best trade off with the accuracy of 86% and the training time is twice faster than the RAKEL algorithm.

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