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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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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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Combined Deep Learning and Classical Potential Approach for Modeling Diffusion in UiO-66

Combined Deep Learning and Classical Potential Approach for ...

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2023 AIChE Annual Meeting

作者： Achar, Siddarth K. Wardzala, Jacob J. Bernasconi, Leonardo Zhang, Linfeng Johnson, J. Karl Department of Chemical & Petroleum Engineering University of Pittsburgh PittsburghPA15261 United States Computational Modeling & Simulation Program University of Pittsburgh PittsburghPA15260 United States Center for Research Computing Department of Chemistry University of Pittsburgh PittsburghPA15260 United States DP Technology Beijing100080 China AI for Science Institute Beijing100080 China

Modeling of diffusion of adsorbates through porous materials with atomistic molecular dynamics (MD) can be a challenging task if the flexibility of the adsorbent needs to be included. This is because potentials need to be developed that accurately account for the motion of the adsorbent in response to the presence of adsorbate molecules. In this work, we show that it is possible to use accurate machine learning atomistic potentials for metal−organic frameworks in concert with classical potentials for adsorbates to accurately compute diffusivities though a hybrid potential approach. As a proof-of-concept, we have developed an accurate deep learning potential (DP) for UiO-66, a metal− organic framework, and used this DP to perform hybrid potential simulations, modeling diffusion of neon and xenon through the crystal. The adsorbate−adsorbate interactions were modeled with Lennard−Jones (LJ) potentials, the adsorbent−adsorbent interactions were described by the DP, and the adsorbent−adsorbate interactions used LJ cross-interactions. Thus, our hybrid potential allows for adsorbent−adsorbate interactions with classical potentials but models the response of the adsorbent to the presence of the adsorbate through near-DFT accuracy DPs. This hybrid approach does not require refitting the DP for new adsorbates. We calculated self-diffusion coefficients for Ne in UiO-66 from DFT-MD, our hybrid DP/LJ approach, and from two different classical potentials for UiO-66. Our DP/LJ results are in excellent agreement with DFT-MD. We modeled diffusion of Xe in UiO-66 with DP/LJ and a classical potential. Diffusion of Xe in UiO-66 is about a factor of 30 slower than that of Ne, so it is not computationally feasible to compute Xe diffusion with DFT-MD. Our hybrid DP−classical potential approach can be applied to other MOFs and other adsorbates, making it possible to use an accurate DP generated from DFT simulations of an empty adsorbent in concert with existing classical potentials for ads

关键词： Molecular dynamics

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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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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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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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The Second Competition on Spatial Statistics for Large Datasets

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Journal of Data Science 2022年第4期20卷 439-460页

作者： Abdulah, Sameh Alamri, Faten Nag, Pratik Sun, Ying Ltaief, Hatem Keyes, David E. Genton, Marc G. Extreme Computing Research Center King Abdullah University of Science and Technology Thuwal 23955-6900 Saudi Arabia Mathematical Science Department Princess Nourah bint Abdulrahman University Riyadh 84428 Saudi Arabia Statistics Program King Abdullah University of Science and Technology Thuwal 23955-6900 Saudi Arabia

In the last few decades, the size of spatial and spatio-temporal datasets in many research areas has rapidly increased with the development of data collection technologies. As a result, classical statistical methods in spatial statistics are facing computational challenges. For example, the kriging predictor in geostatistics becomes prohibitive on traditional hardware architectures for large datasets as it requires high computing power and memory footprint when dealing with large dense matrix operations. Over the years, various approximation methods have been proposed to address such computational issues, however, the community lacks a holistic process to assess their approximation efficiency. To provide a fair assessment, in 2021, we organized the first competition on spatial statistics for large datasets, generated by our ExaGeoStat software, and asked participants to report the results of estimation and prediction. Thanks to its widely acknowledged success and at the request of many participants, we organized the second competition in 2022 focusing on predictions for more complex spatial and spatio-temporal processes, including univariate nonstationary spatial processes, univariate stationary space-time processes, and bivariate stationary spatial processes. In this paper, we describe in detail the data generation procedure and make the valuable datasets publicly available for a wider adoption. Then, we review the submitted methods from fourteen teams worldwide, analyze the competition outcomes, and assess the performance of each team. © 2022 The Author(s).

关键词： Gaussian process multivariate nonstationary prediction space-time spatial

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Multidocument Summarization using GloVe Word Embedding and Agglomerative Cluster Methods 2

Multidocument Summarization using GloVe Word Embedding and A...

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2nd International Conference on Sustainable Engineering and Creative computing, ICSECC 2020

作者： Rosalina Huda, Rafiqul Sahuri, Genta Faculty of Computing Information Technology Study Program President University Bekasi Indonesia

ISBN: (纸本)9781728175881

This paper explores the method of extracting multi-document summary terminology that enhances single document summary methods using the information related to the document and perhaps even the relation between the documents. In the problems of fragmentation, density, duplication and selection of passages, the summarization of several documents ranges from the summarization of single documents to the creation of efficient summaries. Our approach addresses these issues by using Agglomerative cluster sentence, GloVe, TextRank, Cosine Similarity. In addition, this research also use NLTK as library filter word such as stopwords, numeric, punctuation, multiple_whitespaces, short_words in order Vectorizing the sentence when using GloVe. The result of this paper was evaluated using ROUGE;41% for unigram, 17% for bigram, 57% for trigram. © 2020 IEEE.

关键词： Cluster analysis

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Preliminary Design for Reference tracking feedback for Medical Cyber-Physical Pharmacokinetic Systems 26

Preliminary Design for Reference tracking feedback for Medic...

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2024 IEEE International Conference on Automation/26th Congress of the Chilean Association of Automatic Control, ICA-ACCA 2024

作者： Oliveira, Pedro M. Bustos, Daniel Lobo, Rodolfo Lacerda, Marcio J. Palma, Jonathan M. Federal University of Minas Gerais Graduate Program in Electrical Engineering Belo Horizonte Brazil Universidad Católica Del Maule Dept. of Translational Medicine Talca Chile Universidad Diego Portales Ingenieria en Telecomunicaciones e Informática Santiago Chile London Metropolitan University Centre for Communications Technology School of Computing and Digital Media London United Kingdom Universidad de Talca Faculty of Engineering Curicó Chile

ISBN: (纸本)9798350378115

This paper addresses the reference tracking control problem for Medical Cyber-Physical Systems (MCPS). The control theory is employed to guarantee the suitable concentration of drugs in the body of patients to guarantee a safe treatment. The MCPS is modeled as a switched system, and the modes consider the different scenarios for the problem. A discrete-time model is utilized for the pharmacokinetic process, and the zero input control strategy is employed to design state-feedback controllers with a guaranteed exponential convergence rate. A numerical experiment is presented to illustrate the validity and effectiveness of our method. © 2024 IEEE.

关键词： State feedback

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PROCONSUL: PRObabilistic exploration of CONnectivity Significance patterns for disease modULe discovery

PROCONSUL: PRObabilistic exploration of CONnectivity Signifi...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Luca, Riccardo De Carfora, Marco Blanco, Gonzalo Mastropietro, Andrea Petti, Manuela Tieri, Paolo Sapienza University of Rome Data Science Program Rome Italy Sapienza University of Rome DIAG Dept. Comp. Contr. Manag. Eng. Rome Italy IAC Institute for Applied Computing CNR National Research Council Rome Italy

ISBN: (纸本)9781665468190

The possibility to computationally prioritize candidate disease genes capitalizing on existing information has led to a speedup in the discovery of new methods. Many gene discovery techniques exploit network data, like protein-protein interactions (PPIs), in order to extract knowledge from the network structure relying on several network metrics. We here present PROCONSUL, a method that builds on top of the concept of connectivity significance (CS) and exploits the idea of probabilistic exploration of the space of putative disease genes. We show that our methodology is able to outperform the state-of-the-art tool based on CS in several settings, and propose different, effective gene discovery strategies according to specific disease network properties. © 2022 IEEE.

关键词： Proteins

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