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IEEE/ACM Annual Workshop on Large-scale Experiment-in-the-Loop Computing (XLOOP)

作者： Maureen Dougherty Jason Kaelber James Barr von Oehsen Morgan Ludwig Kenneth Dalenberg Jeremy Schafer Michael Zink Boyd Wilson John Goodhue Wolf Hey D. Balamurugan Ecosystem for Research Networking New Brunswick USA Institute for Quantitative Biomedicine Rutgers University New Brunswick USA Office of Advanced Research Computing Rutgers University New Brunswick USA TechSquare Cambridge USA Electrical and Computer Engineering University of Massachusetts Amherst Amherst USA Omnibond Clemson USA Massachusetts Green High Performance Computing Center Holyoke USA Institute for Computational and Data Sciences Pennsylvania State University State College USA

ISBN: (纸本)9781665473613

The Ecosystem for Research networking (ERN) CryoEM Remote Instrument Pilot Project was launched in response to feedback and survey data collected from hundreds of participants of the ERN series of NSF (OAC-2018927) funded community outreach events revealing that Structural Biology instrument driven science is being forced to transition from self-contained islands to federated wide-area internet accessible instruments. Its goal is to facilitate multi-institutional collaboration at the interface of computing and electron microscopy through the implementation of the ERN Federated OpenCI Lab’s Instrument CI Cloudlet design. The conclusion will be a web-based portal leveraging federated access to the instrument, workflows utilizing edge computing in conjunction with cloud computing and real-time monitoring for experimental parameter adjustments and decisions. The intention is to foster team science and scientific innovation, with emphasis on under-represented and under-resourced institutions, through the democratization of these scientific instruments. This paper discusses the latest Phase 1 deployment efforts

关键词： Cloud computing Instruments Ecosystems Collaboration Documentation Real-time systems Software

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Deep Learning Approach for Intelligent Named Entity Recognition of Cyber Security

arXiv

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arXiv 2020年

作者： Simran, K. Sriram, S. Vinayakumar, R. Soman, K.P. Center for Computational Engineering and Networking Amrita School Of Engineering Amrita Vishwa Vidyapeetham Coimbatore India Division of Biomedical Informatics Cincinnati Children’s Hospital Medical Centre CincinnatiOH United States

In recent years, the amount of Cyber Security data generated in the form of unstructured texts, for example, social media resources, blogs, articles, and so on has exceptionally increased. Named Entity Recognition (NER) is an initial step towards converting this unstructured data into structured data which can be used by a lot of applications. The existing methods on NER for Cyber Security data are based on rules and linguistic characteristics. A Deep Learning (DL) based approach embedded with Conditional Random Fields (CRFs) is proposed in this paper. Several DL architectures are evaluated to find the most optimal architecture. The combination of Bidirectional Gated Recurrent Unit (Bi-GRU), Convolutional Neural Network (CNN), and CRF performed better compared to various other DL frameworks on a publicly available benchmark dataset. This may be due to the reason that the bidirectional structures preserve the features related to the future and previous words in a sequence. Copyright © 2020, The Authors. All rights reserved.

关键词： Cybersecurity

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Performance Improvement of Deep Learning Architectures for Phonocardiogram Signal Classification using Fast Fourier Transform

Performance Improvement of Deep Learning Architectures for P...

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International Conference on Advanced Computer Control, ICACC

作者： P. Gopika V. Sowmya E.A. Gopalakrishnan K.P. Soman Center for Computational Engineering and Networking Amrita Vishwa Vidyapeetham Coimbatore India

Phonocardiogram known as PCG plays a significant role in the early diagnosis of cardiac abnormalities. Phonocardiogram can be used as initial diagnostics tool in remote applications due to its simplicity and cost effectiveness. Instead of disease specific approach, the proposed work aims for the single architecture that could diagnose different cardiac abnormality from the PCG signals collected from various sources. Our study also shows the effectiveness of using Fast Fourier Transform (FFT) in signal processing applications. It avoids the trivial preprocessing and feature extraction mechanisms with the promising results.

关键词： diseases fast Fourier transforms feature extraction learning (artificial intelligence) medical signal processing neural nets phonocardiography signal classification

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Capsule Neural Networks and Visualization for Segregation of Plastic and Non-Plastic Wastes

Capsule Neural Networks and Visualization for Segregation of...

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： K. Sreelakshmi S. Akarsh R. Vinayakumar K.P. Soman Center for Computational Engineering and Networking Amrita vishwa vidyapeetham Coimbatore India

Building an image processing model for prediction or classification application has to overcome quite a lot of challenges. Convolutional neural network (CNN) is the pillar of image processing in deep learning perspective. In order to bring down the disadvantages and for improving the performance compared to the CNN, a new architecture of CNN had been devised which is known as Capsule neural network (Capsule-Net). By this paper we analyze Capsule-Net for solid waste management which is separation of plastic and non-plastic. This task is viewed as of at most significance in today's world due to volumes of waste generated and nonavailability of human labor for this work. The capsule-Net is evaluated using 2 different datasets. Dataset 1 represents materials collected from public places and Dataset 2 represents materials collected from private environment. The proposed architecture with capsule-Net gives an accuracy of 96.3% for Dataset 1 and 95.7% for Dataset 2. The necessary hardware setup has been developed and tested. This will be a grace to the society which faces unexplainable difficulty in disposing wastes. It is inexpensive labor free and harmless to health.

关键词： Plastics Sorting Waste materials Radiofrequency identification Deep learning Computer architecture Water conservation

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Eigenvector-based spatial ECG filtering improves QT delineation in stress test recordings

Eigenvector-based spatial ECG filtering improves QT delineat...

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Asilomar Conference on Signals, Systems & Computers

作者： Cristina Pérez Alba Martín-Yebra Jari Viik Juan Pablo Martínez Esther Pueyo Pablo Laguna Biomedical Signal Interpretation and Computational Simulation (BSICoS) group Aragón institute of Engineering Research (I3A) IIS Aragón University of Zaragoza zaragoza Spain Biomedical Research Networking Center in Bioengineering Biomaterials and Nanomedicine(CIBER-BBN) Spain Faculty of Medicine and Health Techonology Tampere University of Technology Tampere Finland

The electrocardiogram signal (ECG) represents the electrical activity of the heart measured at the body surface. Characteristic waves and their delineation marks are studied to define cardiac markers without using invasive procedures. Among them, slower adaptation of the QT interval, the time needed for ventricular depolarization plus repolarization, to sudden abrupt changes in heart rate (HR) has been identified as a marker of arrhythmic risk. Such abrupt HR changes are difficult to induce, leading here to explore estimation of this delay from the ramp-like HR variations observed in exercise stress test. However, stress test ECG signals are often corrupted by muscular activity and electrode motion, limiting the robustness of the information that can be extracted from them, as the identification of the T-wave end. The aim of this study is to find proper methods to emphasize the T-wave in order to improve delineation accuracy. Stress test ECG recordings from 447 subjects were analyzed. The first spatially-transformed lead based on two different lead-space reduction (LSR) techniques, and different learning versions, was used to delineate and obtain the QT series. Assuming that QT delineation errors will lead to a high variability in the QT interval series, the power of the high-pass filtered QT interval series was used as performance marker (the lower the power, the most stable the delineation). Periodic component analysis technique showed the lowest power, with no significant differences between its different learning versions.

关键词： Heart rate Surface waves Electrocardiography Lead Market research Robustness Stress

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Revolutionizing Genomics with Reinforcement Learning Techniques

arXiv

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arXiv 2023年

作者： Karami, Mohsen Jahanian, Khadijeh Alizadehsani, Roohallah Argha, Ahmadreza Dehzangi, Iman Gorriz, Juan M. Zhang, Yu-Dong Hajati, Farshid Yang, Min Alinejad-Rokny, Hamid Amirkabir University of Technology Tehran Iran Faculty of Engineering and IT University of Technology Sydney Sydney Australia Deakin University VIC Australia School of Biomedical Engineering UNSW SYDNEY SydneyNSW2052 Australia Department of Computer Science Rutgers University CamdenNJ08102 United States Center for Computational and Integrative Biology Rutgers University CamdenNJ08102 United States Department of Signal Theory Networking and Communications Universidad de Granada Spain School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom School of Science and Technology Faculty of Science Agriculture Business and Law University of New England ArmidaleNSW2350 Australia Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China School of Biomedical Engineering UNSW SYDNEY SydneyNSW2052 Australia

In recent years, Reinforcement Learning (RL) has emerged as a powerful tool for solving a wide range of problems, including decision-making and genomics. The exponential growth of raw genomic data over the past two decades has exceeded the capacity of manual analysis, leading to a growing interest in automatic data analysis and processing. RL algorithms are capable of learning from experience with minimal human supervision, making them well-suited for genomic data analysis and interpretation. One of the key benefits of using RL is the reduced cost associated with collecting labeled training data, which is required for supervised learning. While there have been numerous studies examining the applications of Machine Learning (ML) in genomics, this survey focuses exclusively on the use of RL in various genomics research fields, including gene regulatory networks (GRNs), genome assembly, and sequence alignment. We present a comprehensive technical overview of existing studies on the application of RL in genomics, highlighting the strengths and limitations of these approaches. We then discuss potential research directions that are worthy of future exploration, including the development of more sophisticated reward functions as RL heavily depends on the accuracy of the reward function, the integration of RL with other machine learning techniques, and the application of RL to new and emerging areas in genomics research. Finally, we present our findings and conclude by summarizing the current state of the field and the future outlook for RL in genomics. © 2023, CC BY-SA.

关键词： Adversarial machine learning

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Semantic analysis using pairwise sentence comparison with word embeddings 3rd

Semantic analysis using pairwise sentence comparison with wo...

引用

3rd International Symposium on Intelligent Systems Technologies and Applications, ISTA 2017

作者： Menon, Vijay Krishna Sabdhi, M. Harikumar, K. Soman, K.P. Center for Computational Engineering and Networking Amrita University Coimbatore India

ISBN: (纸本)9783319683843

Comparing the semantics of a pair of sentences has been an interesting yet unstructured problem. Semantic analysis is mostly elusive due to the fact that the semantics of Natural language constructs cannot be measured, let alone be compared to one another. Methods like Latent Semantic Analysis(LSA) and Latent Dichlaret Analysis(LDA) are able to capture broader semantics between documents, but their contribution in pairwise comparison tasks which require deeper semantics may be limited. In this paper we present a local alignment based scoring scheme for sentence pairs using word embeddings and how this can be used as a feature for some popular text analysis tasks such as summarization, paraphrase comparison, topic profiling and other semantic comparison tasks. We also present a theoretical analysis on the metrics used in this approach and a separability argument using t-SNE plots. Furthermore we detail our Spark implementation model for the pairwise comparison and summarization. © Springer International Publishing AG 2018.

关键词： Semantics

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A Methodology for Spikes and Transients Detection and Removal in Power Signals Using Chebyshev Approximation

A Methodology for Spikes and Transients Detection and Remova...

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International Conference on Advanced Computer Control, ICACC

作者： K Sreelakshmi T Tulasi Sasidhar Neethu Mohan K.P Soman Center for Computational Engineering & Networking (CEN) Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9781728155241

The smart grid is an important empowering agent for a prosperous society. However, due to the involvement of various renewable energy sources, power electronic devices and loads, the grid is prone to distortions which reduce the power quality. It is very important to improve the quality of the power signal as it can damage the equipment that consumes it. In this paper, a novel and robust method is developed to improve the power quality by using Chebyshev approximation. The proposed methodology detects the spike and transient distortions in power signals and removes them effectively. The efficacy of the proposed method is tested over different noise intensities and also compared with a VMD based signal smoothing system. The promising results evince that the system can be used for power quality improvement in smart grid environment. This method is useful for predicting the exact locations and magnitudes of disturbances in future time so that control/corrective actions can be taken to rectify the distortions.

关键词： Chebyshev approximation power electronics power generation control power supply quality power system transients renewable energy sources smart power grids Power quality Smart grids Chebyshev approximation power generation control power system transients Renewable energy renewable energy sources Power electronics signal power

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Data-driven Analysis for Low Frequency Oscillation Identification in Smart Grid using FB-DMD and T-DMD Methods

Data-driven Analysis for Low Frequency Oscillation Identific...

引用

International Conference on Advanced Computer Control, ICACC

作者： V.T Priyanga M Chandni Neethu Mohan K.P Soman Center for Computational Engineering & Networking (CEN) Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9781728155241

The major challenges faced by the modern interconnected grids are the disturbances caused due to the presence of the low frequency oscillations (LFO). These inter-area modes can cause a huge disturbances and may also lead to the failure of the entire electric grid system. Hence, monitoring of these low frequency oscillations and mitigating its effects are very much important to maintain stability of the power system. To employ a better control over the grid and to bring up the stability of the system, the damped modes of the power signal generated must be known even under a noisy condition. Based on the past literature, dynamic mode decomposition (DMD) method has proven to be the best to identify the damped modes of a signal. However, it fails to give satisfying results in the presence of noises. Hence, two variants of DMD namely forward-backward DMD (FB-DMD) and total-DMD (T-DMD) are adapted in this paper to find the dynamic modes of the system which helps to identify the low frequency modes even under a noisy condition. Based on the experimental results and analysis, T-DMD is found to be suitable to predict the low frequency modes with higher accuracy and lower complexity.

关键词： damping oscillations power generation control power system interconnection power system stability smart power grids Smart grids Power system stability power generation control power system interconnection absorption of shock Oscillations low-frequency oscillation low frequencies DMD gene signal power Noise

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Convolutional Neural Networks for Placenta Cell Classification

Convolutional Neural Networks for Placenta Cell Classificati...

引用

International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT)

作者： E Rohith V Sowmya K P Soman Center for Computational Engineering and Networking (CEN) Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India Center for Computational Engineering and Networking (CEN) Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore Tamil Nadu India

The placenta is a complicated organ that plays several roles during fetal evolution. There was a little awareness regarding the connection of anatomical placental disorders with fetal biology. In this particular work, we introduce an open source mathematically traceable deep learning pipeline to examine cell-level placenta histology using neural convolution networks. Also, we have a tendency to learn deep embedded encoding makeup information that's capable of each stratifying 5 distinct cell populations and learn interclass makeup variance. We anticipate that the automation of such a pipeline to population-scale placenta histology research does have the potential to benefit our understanding of the basic cellular placental biology, substantially its role in forecasting adverse birth results. The objective of the present work is to classify the cell populations into 5 classes using the convolution neural network.

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