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Proceedings of the Winter Simulation Conference

作者： Jonas F. Leon Paolo Marone Mohammad Peyman Yuda Li Laura Calvet Mohammad Dehghanimohammadabadi Angel A. Juan Spindox España S.L. Universitat Oberta de Catalunya Barcelona SPAIN Computer Science Dept. Universitat Oberta de Catalunya Barcelona SPAIN Dept. of Mechanical and Industrial Engineering Northeastern University Boston MA USA Dept. of Applied Statistics & Operations Research Universitat Politècnica de València Alcoy SPAIN

Connecting commercial discrete-event simulation packages to external software or programming languages is essential to advance simulation modeling capabilities. For instance, this connectivity allows users to link the simulation environment to metaheuristic optimization algorithms or to machine learning methods. However, implementing these connections is not a trivial task, and may require API access and proper settings configurations. This tutorial provides a step-by-step guideline to connect the FlexSim commercial simulator with the popular Python programming language via sockets. Using this type of connection, a simheuristic algorithm coded in Python aims at optimizing the product allocation in a warehouse, which has been previously modeled in the aforementioned simulator. In addition, potential future applications of this software combination will be discussed to provide insights into future developments such as more advanced simheuristics or combinations of simulation with learnheuristics.

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Probability Distribution on Rooted Trees

Probability Distribution on Rooted Trees

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IEEE International Symposium on Information Theory

作者： Yuta Nakahara Shota Saito Akira Kamatsuka Toshiyasu Matsushima Center for Data Science Waseda University Tokyo Japan Faculty of Informatics Gunma University Gunma Japan Dept. of Information Science Shonan Institute of Technology Kanagawa Japan Dept. of Applied Math. Waseda University Tokyo Japan

ISBN: (数字)9781665421591

ISBN: (纸本)9781665421607

The hierarchical and recursive expressive capability of rooted trees is applicable to represent statistical models in various areas, such as data compression, image processing, and machine learning. On the other hand, such hierarchical expressive capability causes a problem in tree selection to avoid overfitting. One unified approach to solve this is a Bayesian approach, on which the rooted tree is regarded as a random variable and a direct loss function can be assumed on the selected model or the predicted value for a new data point. However, all the previous studies on this approach are based on the probability distribution on full trees, to the best of our knowledge. In this paper, we propose a generalized probability distribution for any rooted trees in which only the maximum number of child nodes and the maximum dept. are fixed. Furthermore, we derive recursive methods to evaluate the characteristics of the probability distribution without any approximations.

关键词： Image processing Data compression Machine learning Predictive models Approximation algorithms Probability distribution Data models

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ARPIST: Provably Accurate and Stable Numerical Integration over Spherical Triangles

arXiv

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arXiv 2022年

作者： Li, Yipeng Jiao, Xiangmin Dept. of Applied Math. & Stat. Institute for Advanced Computational Science Stony Brook University Stony BrookNY11794 United States

Numerical integration on spheres, including the computation of the areas of spherical triangles, is a core computation in geomathematics. The commonly used techniques sometimes suffer from instabilities and significant loss of accuracy. We describe a new algorithm, called ARPIST, for accurate and stable integration of functions on spherical triangles. ARPIST is based on an easy-to-implement transformation to the spherical triangle from its corresponding linear triangle via radial projection to achieve high accuracy and efficiency. More importantly, ARPIST overcomes potential instabilities in computing the Jacobian of the transformation, even for poorly shaped triangles that may occur at poles in regular longitude-latitude meshes, by avoiding potential catastrophic rounding errors. We compare our proposed technique with L’Huilier’s Theorem for computing the area of spherical triangles, and also compare it with the recently developed LSQST method (J. Beckmann, H.N. Mhaskar, and J. Prestin, GEM - Int. J. Geomath., 5:143–162, 2014) and a radial-basis-function-based technique (J. A. Reeger and B. Fornberg, Stud. Appl. math., 137:174–188, 2015) for integration of smooth functions on spherical triangulations. Our results show that ARPIST enables superior accuracy and stability over previous methods while being orders of magnitude faster and significantly easier to implement. Copyright © 2022, The Authors. All rights reserved.

关键词： Integration

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Electroencephalogram Processing Method for More Accurate Determination of Cognitive Process Indicators

Electroencephalogram Processing Method for More Accurate Det...

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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Vitaliy S. Karmanov Elena A. Sapina Alla A. Karmanova Andrey A. Kriveckiy Dept. of Theoretical and Applied Computer Science Novosibirsk state technical university Novosibirsk Russian Federation Federal Research Center for Basic and Translational Medicine Novosibirsk State University of Economics and Management Novosibirsk Russian Federation Correspondence dept Novosibirsk state technical university Novosibirsk Russian Federation

ISBN: (数字)9798350389234

ISBN: (纸本)9798350353051

To study neurophysiological mechanisms of functioning of the nervous system and psyche, modern computer technologies and methods of mathematical data processing are used. One of the non-invasive methods of determining the functional state of the brain is electroencephalography. The use of the spectral singular analysis method, together with the traditional approach using Fourier analysis, is proposed to determine the indicators of cognitive processes. Computation of the singular spectrum is a rather computationally expensive process, but modern technologies allow to perform capacious computations with acceptable speed and accuracy, which allows to use new methods of data analysis. The proposed approach allows us to decompose the original signal into elementary components, which are most often described by quasi-harmonic functions. It is further proposed to perform nonparametric robust estimation of frequencies, based on which some elementary components can be grouped on the basis of close spectral characteristics. The results of calculations on the experimental data are presented, and the change of amplitude and frequency of alpha rhythm for the case of open and closed eyes of the subject is shown. As a result, the proposed method makes it possible to clarify the main characteristics of electroencephalogram signals.

关键词： Accuracy Data analysis Cognitive processes Estimation Rhythm Nervous system Frequency estimation

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Experience Report on Key Success Factors for Promoting Students' Engagement in Software Development Group Projects 4

Experience Report on Key Success Factors for Promoting Stude...

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4th IEEE World Engineering Education Conference, EDUNINE 2020

作者： Schorr, Ruth Frankfurt University of Applied Sciences Dept. of Computer Science and Engineering Frankfurt am Main Germany

ISBN: (纸本)9781728166384

To teach a software development group project course can be a challenging task. Several large-scale surveys on students' engagement indicate that computer science rates lower than average in many of the major benchmarks. This paper presents a framework that consist of a variety of factors for setting up software development project courses for computer science bachelor students at Frankfurt University of applied sciences in their final year. Groups of 3 to 6 students have to develop an executable system for an existing realistic problem by going through all phases of the Software Development Life Cycle (SDLC). The paper presents the advantages and disadvantages as well as the dependencies between the various factors. Special emphasis is paid on the impact of these factors on students' work and learning motivation. The paper is concluded with a short review of the proposed teaching strategy and the results received. © 2020 IEEE.

关键词： Students

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Cloud-Based Smart Plant Watering System using IOT-A Review

Cloud-Based Smart Plant Watering System using IOT-A Review

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IEEE UP Section Conference on Electrical computer and Electronics (UPCON)

作者： Yammanuru Chaitanya Reddy Vuppu Surya Prakash Gajjala Sri Vardhan Reddy Anjana Snehith Santhosh Ruchitha Reddy Jagdish Chandra Patni Sudhir Kumar Chaturvedi Nilesh Bahadure Dept. of CSE Alliance School of Advanced Computing Alliance University Bengaluru India Department of Aerospace Engineering Alliance School of Applied Engineering Alliance University Bengaluru India Department of computer science and engineering school of technology GSFC University Vadodara Gujarat India

ISBN: (数字)9798350378726

ISBN: (纸本)9798350378733

Water management in agriculture needs to be done efficiently because of the increasing scarcity of water in the world. Through the design and implementation of a Smart Plant Watering System with Cloud Analysis, this study offers a novel solution to this problem. The main goal of this paper is to use cloud-based analytics and Internet of Things (IoT) technology to optimize water usage while maintaining plant health. Our study suggests an effective method from the existing methods, that incorporates sensors to track important environmental variables, particularly the nearby plant's temperature and moisture content. The processes use the sensor readings in real time and depending on pre-established thresholds, activates an electric motor to release water when it is required.

关键词： Temperature sensors Cloud computing Irrigation Wireless sensor networks Plants (biology) Water conservation Internet of Things Stakeholders Intelligent sensors Stress

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Exploring Contextualized Embeddings for C++ Code Search and Algorithms Classification

Exploring Contextualized Embeddings for C++ Code Search and ...

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International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Robert R. Khazhiev Darya A. Skvortsova Sergey N. Golubev Lev R. Barabanshchikov Industrial Logistic Department Bauman Moscow State Technical University Moscow Russian Federation Dept. of Information Processing and Management Systems Bauman Moscow State Technical University Moscow Russian Federation Institute of Applied Computer Science ITMO University Saint-Petersburg Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

Contextualized embeddings of words (such as BLOOM and BERT) outperform static embeddings (such as Word2vec, Fasttext, and GloVe) for many natural language processing tasks. In this article, an exploration is conducted of the use of contextualized embeddings for the purposes of searching for C++ code and classifying the algorithms that are utilized. Furthermore, a novel approach is proposed for reducing the dimensionality of the resulting embeddings, leveraging a multilayer perceptron trained with Multi Similarity Loss. The experiments performed for the methods of multilabel classification of algorithms and for the code similarity search show that the proposed method not only effectively reduces the dimension of embeddings, but also increases their representativeness.

关键词： Measurement Power engineering Codes Source coding Large language models C++ languages Multi label classification Multilayer perceptrons Natural language processing

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Time-domain Li-ion Battery Modeling under Staircase Charging and Discharging

Time-domain Li-ion Battery Modeling under Staircase Charging...

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2021 International Conference on Microelectronics, ICM 2021

作者： Abdelhafiz, Shahenda M. Abdelaty, A.M. Fouda, M.E. Radwan, A.G. Giza Egypt Fayoum University Faculty of Engineering Engineering Mathematics and Physics Dept. Egypt Cairo University Faculty of Engineering Engineering Mathematics and Physics Dept. Egypt University of California-Irvine Electrical Engineering and Computer Science Dept. Irvine United States School of Engineering and Applied Sciences Nile University Giza Egypt

ISBN: (纸本)9781665408394

Parameter identification of Li-ion battery models is important for efficiently charge and discharge the most widely used energy storage devices. In this work, we propose a simplified battery model with a parameter identification method for time-domain charging and discharging. Staircase PotentioElectrochemical Impedance Spectroscopy technique (SPEIS) is chosen to characterize the batteries during charging and discharging cycles at different voltage steps values. Marine Predator Algorithm (MPA) is used to identify the proposed model parameters on two commercial Li-ion coin-shaped batteries. The proposed model shows very good matching with the experiments with absolute current error less than 10 4. Hence, the proposed model can be used for real-time applications to predict the battery's behavior under different operating conditions. © 2021 IEEE.

关键词： Lithium-ion batteries

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GPT (Generative Pre-trained Transformer) - A Comprehensive Review on Enabling Technologies, Potential Applications, Emerging Challenges, and Future Directions

arXiv

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arXiv 2023年

作者： Yenduri, Gokul Ramalingam, M. Selvi, Chemmalar G. Supriya, Y. Srivastava, Gautam Maddikunta, Praveen Kumar Reddy Raj, Deepti G. Jhaveri, Rutvij H. Prabadevi, B. Wang, Weizheng Vasilakos, Athanasios V. Gadekallu, Thippa Reddy The School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India The Dept. of Math and Computer Science Brandon University Canada The Research Centre for Interneural Computing China Medical University Taichung Taiwan Dept. of Computer Science and Math Lebanese American University Beirut Lebanon The Department of Computer Science and Engineering School of Technology Pandit Deendayal Energy University India The Department of Computer Science City University of Hong Kong Hong Kong Grimstad Norway Lovely Professional University Phagwara India Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Jiaxing University Jiaxing314001 China Zhongda Group 314312 China

The Generative Pre-trained Transformer (GPT) represent a notable breakthrough in the domain of natural language processing, which is propelling us toward the development of machines that can understand and communicate using language in a manner that closely resembles that of humans. GPT is based on the transformer architecture, a deep neural network designed for natural language processing tasks. Due to their impressive performance on natural language processing tasks and ability to effectively converse, GPT have gained significant popularity among researchers and industrial communities, making them one of the most widely used and effective models in natural language processing and related fields, which motivated to conduct this review. This review provides a detailed overview of the GPT, including its architecture, working process, training procedures, enabling technologies, and its impact on various applications. In this review, we also explored the potential challenges and limitations of a GPT. Furthermore, we discuss potential solutions and future directions. Overall, this paper aims to provide a comprehensive understanding of GPT, enabling technologies, their impact on various applications, emerging challenges, and potential solutions. © 2023, CC BY.

关键词： Deep neural networks

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Information Theoretic-based Feature Selection (IFS) for Machine Learning 1

Information Theoretic-based Feature Selection (IFS) for Mach...

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1st International Conference on Multidisciplinary Engineering and applied science, ICMEAS 2021

作者： Suleiman, Muhammad A. Labadin, Jane Nile University of Nigeria Faculty of Natural and Applied Sciences Dept. of Software Engineering and Information Technology Abuja Nigeria University Malaysia Sarawak Faculty of Computer Science and Information Technology Kota Samarahan Malaysia

ISBN: (纸本)9781665434935

Mutual Information (MI) has been a popular choice for the selection criterion function of the feature selection techniques. However, only a handful of methods of formulating MI estimator for regression tasks exist, despite the existence of numerous methods of MI estimation for classification tasks in the literature. This work proposed a feature selection method based on mutual information for both classification and regression tasks. Unlike the existing MI estimator for regression tasks which are widely non-parametric, the proposed method provides a suitable way to estimate MI based on conventional kernel density estimation numerically. Two different experiments were conducted to evaluate the proposed IFS method. The first test is designed to assess the correctness of the formulated MI estimator by comparing it with various existing benchmarks. The second experiment evaluates the proposed method using natural domain data. Overall results obtained from these experiments show that the proposed method was successfully able to select relevant features. © 2021 IEEE.

关键词： Feature Selection

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